Removed warning about TF2.0 in the readme

- Sorta works
 - Frontend needs work, but functionality is there

.gitignore

@@ -104,13 +104,22 @@ venv.bak/
 .mypy_cache/
 
 #screenshot data
-*.png
-*.jpg
-*.tar
+images*
+
+#Graphs and Results
+graphs/*
 
 #model files
 *.h5
 *.model
 *.csv
 *.txt
+results.html
 !requirements.txt
+!requirements-gpu.txt
+!tests/models/*
+!tests/data/*
+!docs/*
+
+#Leave the webui things alone for their own gitignore
+!webui/*

README.md

@@ -0,0 +1,104 @@
+# Eyeballer
+Give those screenshots of yours a quick eyeballing.
+
+Eyeballer is meant for large-scope network penetration tests where you need to find "interesting" targets from a huge set of web-based hosts. Go ahead and use your favorite screenshotting tool like normal (EyeWitness or GoWitness) and then run them through Eyeballer to tell you what's likely to contain vulnerabilities, and what isn't.
+
+
+### Example Labels
+
+| Old-Looking Sites | Login Pages |
+| ------ |:-----:|
+| ![Sample Old-looking Page](/docs/old-looking.png) | ![Sample Login Page](/docs/login.png) |
+
+| Homepages | Custom 404's |
+| ------ |:-----:|
+| ![Sample Homepage](/docs/homepage.png) | ![Sample Custom 404](/docs/404.png) |
+
+## Web UI Setup
+
+Eyeballer's Web UI uses TensorflowJS and React. It runs entirely in your browser locally, with no backend functionality whatsoever. So you can "upload" files to classify and get results without the files ever leaving your computer. To run the Eyeballer Web UI locally, you'll need Node. On Debian, you can install that with the npm package:
+
+```
+sudo apt install npm
+```
+
+Then you'll need to install some Node dependencies:
+
+```
+cd webui/
+npm install
+```
+
+Copy a pre-trained model file (in TensorflowJS format) into a jsmodel/ folder under public/, so that it's available in the web server.
+
+```
+cp YOUR_MODEL/ webui/public/
+```
+
+Then you can run the Web UI with:
+
+```
+npm start
+```
+
+## Python Setup
+
+Download required packages on pip:
+```
+sudo pip3 install -r requirements.txt
+```
+
+Or if you want GPU support:
+```
+sudo pip3 install -r requirements-gpu.txt
+```
+
+**NOTE**: Setting up a GPU for use with TensorFlow is way beyond the scope of this README. There's hardware compatibility to consider, drivers to install... There's a lot. So you're just going to have to figure this part out on your own if you want a GPU. But at least from a Python package perspective, the above requirements file has you covered.
+
+**Training Data** You can find our training data here:
+
+https://www.dropbox.com/sh/7aouywaid7xptpq/AAD_-I4hAHrDeiosDAQksnBma?dl=1
+
+Pretty soon, we're going to add this as a TensorFlow DataSet, so you don't need to download this separately like this. It'll also let us version the data a bit better. But for now, just deal with it. There's two things you need from the training data:
+
+1. `images/` folder, containing all the screenshots (resized down to 224x140. We'll have the full-size images up soon)
+2. `labels.csv` that has all the labels
+3. `bishop-fox-pretrained-v1.h5` A pretrained weights file you can use right out of the box without training.
+
+Copy all three into the root of the Eyeballer code tree.
+
+## Predicting Labels
+To eyeball some screenshots, just run the "predict" mode:
+
+```
+eyeballer.py --model YOUR_MODEL.h5 predict YOUR_FILE.png
+```
+
+Or for a whole directory of files:
+
+```
+eyeballer.py --model YOUR_MODEL.h5 predict PATH_TO/YOUR_FILES/
+```
+
+Eyeballer will spit the results back to you in human readable format (a `results.html` file so you can browse it easily) and machine readable format (a `results.csv` file).
+
+
+## Training
+To train a new model, run:
+```
+eyeballer.py train
+```
+
+You'll want a machine with a good GPU for this to run in a reasonable amount of time. Setting that up is outside the scope of this readme, however.
+
+This will output a new model file (model.h5 by default).
+
+## Evaluation
+
+You just trained a new model, cool! Let's see how well it performs against some images it's never seen before, across a variety of metrics:
+
+```
+eyeballer.py --model YOUR_MODEL.h5 evaluate
+```
+
+The output will describe the model's accuracy in both recall and precision for each of the program's labels. (Including "none of the above" as a pseudo-label)

eyeballer.py

@@ -1,17 +1,20 @@
 #!/usr/bin/env python3
+
 import click
 import csv
 
-from model import EyeballModel, DATA_LABELS
+from eyeballer.model import EyeballModel, DATA_LABELS
+from eyeballer.visualization import HeatMap
+from jinja2 import Template
 
 
 @click.group(invoke_without_command=True)
-@click.option('--weights', default=None, type=click.Path(), help="Weights file for input/output")
+@click.option('--model', default=None, type=click.Path(), help="Model file for input/output")
 @click.option('--summary/--no-summary', default=False, help="Print model summary at start")
 @click.option('--seed', default=None, type=int, help="RNG seed for data shuffling and transformations, defaults to random value")
 @click.pass_context
-def cli(ctx, weights, summary, seed):
-    model_kwargs = {"weights_file": weights,
+def cli(ctx, model, summary, seed):
+    model_kwargs = {"model_file": model,
                     "print_summary": summary,
                     "seed": seed}
 
@@ -34,10 +37,17 @@ def train(ctx, graphs, batchsize, epochs):
 
 @cli.command()
 @click.argument('screenshot')
+@click.option('--heatmap', default=False, is_flag=True, help="Create a heatmap graphfor the prediction")
+@click.option('--threshold', default=.5, type=float, help="Threshold confidence for labeling")
 @click.pass_context
-def predict(ctx, screenshot):
+def predict(ctx, screenshot, heatmap, threshold):
     model = EyeballModel(**ctx.obj['model_kwargs'])
     results = model.predict(screenshot)
+
+    if heatmap:
+        # Generate a heatmap
+        HeatMap(screenshot, model, threshold).generate()
+
     if not results:
         print("Error: Input file does not exist")
     if len(results) == 1:
@@ -48,18 +58,60 @@ def predict(ctx, screenshot):
             labelwriter = csv.DictWriter(csvfile, fieldnames=fieldnames)
             labelwriter.writeheader()
             labelwriter.writerows(results)
+
         print("Output written to results.csv")
+        buildHTML(processResults(results, threshold))
+        print("HTML written to results.html")
+
+
+def processResults(results, threshold):
+    '''Filter the initial results dictionary and reformat it for use in JS.
+
+        Keyword arguments:
+        results -- dictionary output from predict function
+
+    '''
+    jsResults = {}
+
+    for result in results:
+        positiveTags = []
+
+        for label, label_info in result.items():
+            if (label == 'filename'):
+                pass
+            elif label_info > threshold:
+                positiveTags.append(label)
+
+        jsResults[result['filename']] = positiveTags
+    return(jsResults)
+
+
+def buildHTML(jsResults):
+    '''Build HTML around the JS Dictionary that is passed from processResults.
+
+        Keyword arguments:
+        jsResults -- dictionary output from processResults function
+    '''
+    html_output = ""
+    with open("prediction_output_template.html") as template_file:
+        template = Template(template_file.read())
+        html_output = template.render(jsResults=jsResults)
+
+    with open('results.html', 'w') as file:
+        file.write(html_output)
 
 
 def pretty_print_evaluation(results):
     """Print a human-readable summary of the evaluation"""
     # We use 4.2% to handle all the way from "  0.00%" (7chars) to "100.00%" (7chars)
     for label in DATA_LABELS:
-        print(f"{label} Precision Score: {results[label]['precision']:4.2%}")
-        print(f"{label} Recall Score: {results[label]['recall']:4.2%}")
-    print(f"Overall Binary Accuracy: {results['total_binary_accuracy']:4.2%}")
-    print(f"All or nothing accuracy: {results['all_or_nothing_accuracy']:4.2%}")
-    print(f"Top 10 worst predictions: {results['top_10_worst'][1]}")
+        print("{} Precision Score: {:4.2%}".format(label, results[label]['precision']))
+        print("{} Recall Score: {:4.2%}".format(label, results[label]['recall']))
+    print("'None of the above' Precision: {:4.2%}".format(results['none_of_the_above_precision']))
+    print("'None of the above' Recall: {:4.2%}".format(results['none_of_the_above_recall']))
+    print("All or nothing Accuracy: {:4.2%}".format(results['all_or_nothing_accuracy']))
+    print("Overall Binary Accuracy: {:4.2%}".format(results['total_binary_accuracy']))
+    print("Top 10 worst predictions: {}".format(results['top_10_worst'][1]))
 
 
 @cli.command()

eyeballer/__init__.py

@@ -1 +0,0 @@
-from eyeballer.eyeballer import EyeballModel  # noqa: F401

eyeballer/augmentation.py

@@ -1,7 +1,7 @@
 import numpy as np
 
 from Augmentor.Operations import Operation
-from keras.applications.mobilenet import preprocess_input
+from tensorflow.keras.applications.mobilenet import preprocess_input
 
 
 class EyeballerAugmentation(Operation):

eyeballer/model.py

@@ -1,5 +1,7 @@
 import os
 import random
+import sys
+import progressbar
 
 # Prevent Tkinter Dependency
 import matplotlib
@@ -9,75 +11,57 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import Augmentor
+import tensorflow as tf
 
-from keras import regularizers
-from keras.applications import MobileNet
-from keras.applications.mobilenet import preprocess_input
-from keras.callbacks import ModelCheckpoint
-from keras.layers import Dropout, Dense
-from keras.layers import GlobalAveragePooling2D
-from keras.models import Model
-from keras.optimizers import Adam
-from keras.preprocessing import image
-from keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.applications.mobilenet import preprocess_input
 from sklearn.metrics import classification_report, accuracy_score, hamming_loss
-from augmentation import EyeballerAugmentation
-from keras.callbacks import Callback
+from eyeballer.augmentation import EyeballerAugmentation
 
 DATA_LABELS = ["custom404", "login", "homepage", "oldlooking"]
 
 
-class PredictionManager(Callback):
-    def __init__(self):
-        super(PredictionManager, self).__init__()
-        self.predictions = []
-
-    def getPredictions(self):
-        return np.array(self.predictions)
-
-    def on_predict_batch_end(self, batch, logs=None):
-        self.predictions.append(logs['outputs'][0].tolist()[0])
-
-
 class EyeballModel:
     """The primary model class of Eyeballer.
 
     Contains high-level functions for training, evaluating, and predicting.
     """
     graphs_directory = "graphs/"
-    checkpoint_file = "weights.h5"
-    image_dir = "images_224x224/"
+    checkpoint_file = "model.h5"
+    image_dir = "images/"
     image_width, image_height = 224, 224
     input_shape = (image_width, image_height, 3)
 
-    def __init__(self, print_summary=False, weights_file="weights.h5", seed=None):
+    def __init__(self, model_file, print_summary=False, seed=None, quiet=False):
         """Constructor for model class.
 
         Keyword arguments:
         print_summary -- Whether or not to print to stdout the keras model summary, containing a detailed description of every model layer
-        weights_file -- A filename for where to load the model's weights.
+        model_file -- A filename for where to load the model's model.
         seed -- PRNG seed, useful for repeating a previous run and using the same data. Training/Validation split is determined randomly.
         """
-        # Build the model
-        base_model = MobileNet(weights='imagenet', include_top=False, input_shape=self.input_shape)
-        x = base_model.output
-        x = GlobalAveragePooling2D()(x)
-        x = Dense(256, activation="relu", name="HiddenLayer1")(x)
-        x = Dropout(0.5)(x)
-        x = Dense(128, activation="relu", name="HiddenLayer2", kernel_regularizer=regularizers.l2(0.01))(x)
-        x = Dropout(0.2)(x)
-        output_layer = Dense(len(DATA_LABELS), activation="sigmoid", name="OutputLayer")(x)
-        self.model = Model(inputs=base_model.input, outputs=output_layer)
+        # # Build the model
+        self.model = tf.keras.Sequential()
+        pretrained_layer = tf.keras.applications.mobilenet.MobileNet(weights='imagenet', include_top=False, input_shape=self.input_shape)
+        self.model.add(pretrained_layer)
+        self.model.add(tf.keras.layers.GlobalAveragePooling2D())
+        self.model.add(tf.keras.layers.Dense(256, activation="relu"))
+        self.model.add(tf.keras.layers.Dropout(0.5))
+        self.model.add(tf.keras.layers.Dense(128, activation="relu"))
+        self.model.add(tf.keras.layers.Dropout(0.2))
+        self.model.add(tf.keras.layers.Dense(len(DATA_LABELS), activation="sigmoid"))
 
-        # for layer in base_model.layers:
+        self.model.compile(optimizer=tf.keras.optimizers.Adam(0.0005),
+                           loss="binary_crossentropy",
+                           metrics=["accuracy"])
+
+        # for layer in pretrained_layer.layers:
         #     layer.trainable = False
 
-        adam = Adam(lr=0.0005)
-        self.model.compile(optimizer=adam, loss="binary_crossentropy", metrics=["accuracy"])
-
         if print_summary:
             print(self.model.summary())
 
+        self.quiet = quiet
+
         # Pull out our labels for use in generators later
         data = pd.read_csv("labels.csv")
         self.training_labels = data.loc[data['evaluation'] == False]  # noqa: E712
@@ -90,15 +74,21 @@ class EyeballModel:
             self.random_seed = True
             self.seed = random.randint(0, 999999)
             print("No seed set, ", end='')
-        print(f"using seed: {self.seed}")
+        print("using seed: {}".format(self.seed))
         random.seed(self.seed)
         self.training_labels = self.training_labels.sample(frac=1)
 
-        if weights_file is not None and os.path.isfile(weights_file):
-            self.model.load_weights(weights_file)
+        if model_file is not None and os.path.isfile(model_file):
+            try:
+                self.model = tf.keras.models.load_model(model_file)
+            except OSError:
+                print("ERROR: Unable to open model file '{}'".format(model_file))
+                sys.exit(-1)
             print("Loaded model from file.")
         else:
-            print("No model loaded from file")
+            if model_file is not None:
+                raise FileNotFoundError
+            print("WARN: No model loaded from file. Generating random model")
 
         # Data augmentation
         augmentor = Augmentor.Pipeline()
@@ -115,7 +105,7 @@ class EyeballModel:
         self.preprocess_training_function = augmentor.keras_preprocess_func()
 
     def train(self, epochs=20, batch_size=32, print_graphs=False):
-        """Train the model, making a new weights file at each successfull checkpoint. You'll probably need a GPU for this to realistically run.
+        """Train the model, making a new model file at each successfull checkpoint. You'll probably need a GPU for this to realistically run.
 
         Keyword arguments:
         epochs -- The number of epochs to train for. (An epoch is one pass-through of the dataset)
@@ -124,7 +114,7 @@ class EyeballModel:
         """
         print("Training with seed: " + str(self.seed))
 
-        data_generator = ImageDataGenerator(
+        data_generator = tf.keras.preprocessing.image.ImageDataGenerator(
             preprocessing_function=self.preprocess_training_function,
             validation_split=0.2,
             samplewise_center=True)
@@ -152,9 +142,13 @@ class EyeballModel:
             seed=self.seed,
             class_mode="other")
 
-        # Model checkpoint - Saves model weights when validation accuracy improves
-        callbacks = [ModelCheckpoint(self.checkpoint_file, monitor='val_loss', verbose=1, save_best_only=True,
-                                     save_weights_only=True, mode='min')]
+        # Model checkpoint - Saves model when validation accuracy improves
+        callbacks = [tf.keras.callbacks.ModelCheckpoint(self.checkpoint_file,
+                     monitor='val_loss',
+                     verbose=1,
+                     save_best_only=True,
+                     save_weights_only=False,
+                     mode='min')]
 
         history = self.model.fit_generator(
             training_generator,
@@ -189,11 +183,29 @@ class EyeballModel:
             plt.legend(['Train', 'Validation'], loc='upper left')
             plt.savefig(self.graphs_directory + "loss.png")
 
-    def predict(self, path):
-        """Predict the labels for a single file
+    def predict_on_array(self, image):
+        """Predict the labels for a single screenshot
 
         Keyword arguments:
-        path -- The path to the file that we'll be evaluating
+        image -- The numpy array of the image to classify
+        """
+        img = np.expand_dims(image, axis=0)
+        img = preprocess_input(img)
+
+        prediction = self.model.predict(img, batch_size=1)
+        result = dict()
+        result["filename"] = "custom-image"
+        result["custom404"] = prediction[0][0]
+        result["login"] = prediction[0][1]
+        result["homepage"] = prediction[0][2]
+        result["oldlooking"] = prediction[0][3]
+        return result
+
+    def predict(self, path, threshold=0.5):
+        """Predict the labels for a single file or directory of files
+
+        Keyword arguments:
+        path -- The path to the file(s) that we'll be evaluating.
         """
         # Is this a single file, or a directory?
         screenshots = []
@@ -203,15 +215,26 @@ class EyeballModel:
             screenshots = os.listdir(path)
             screenshots = [os.path.join(path, s) for s in screenshots]
         else:
-            return None
+            raise FileNotFoundError
 
         results = []
-        for screenshot in screenshots:
+        bar = progressbar.ProgressBar()
+        if self.quiet:
+            bar = progressbar.NullBar()
+        for screenshot in bar(screenshots):
             # Load the image into memory
-            img = image.load_img(screenshot, target_size=(self.image_width, self.image_height))
-            img = image.img_to_array(img)
-            img = np.expand_dims(img, axis=0)
-            img = preprocess_input(img)
+            img = None
+            try:
+                img = tf.keras.preprocessing.image.load_img(screenshot, target_size=(self.image_width, self.image_height))
+                img = tf.keras.preprocessing.image.img_to_array(img)
+                img = np.expand_dims(img, axis=0)
+                img = preprocess_input(img)
+            except IsADirectoryError:
+                print("\nWARN: Skipping directory: ", screenshot)
+                continue
+            except OSError:
+                print("\nWARN: Skipping empty or corrupt file: ", screenshot)
+                continue
 
             prediction = self.model.predict(img, batch_size=1)
             result = dict()
@@ -221,7 +244,6 @@ class EyeballModel:
             result["homepage"] = prediction[0][2]
             result["oldlooking"] = prediction[0][3]
             results.append(result)
-
         return results
 
     def evaluate(self, threshold=0.5):
@@ -231,7 +253,7 @@ class EyeballModel:
         threshold -- Value between 0->1. The cutoff where the numerical prediction becomes boolean. Default: 0.5
         """
         # Prepare the data
-        data_generator = ImageDataGenerator(
+        data_generator = tf.keras.preprocessing.image.ImageDataGenerator(
             preprocessing_function=preprocess_input)
         evaluation_generator = data_generator.flow_from_dataframe(
             self.evaluation_labels,
@@ -246,7 +268,7 @@ class EyeballModel:
         if not self.random_seed:
             print("Using validation set...")
             # Data augmentation
-            data_generator = ImageDataGenerator(
+            data_generator = tf.keras.preprocessing.image.ImageDataGenerator(
                 preprocessing_function=self.preprocess_training_function,
                 samplewise_center=True,
                 validation_split=0.2)
@@ -264,44 +286,70 @@ class EyeballModel:
         else:
             print("Using evaluation set...")
 
-        prediction_manager = PredictionManager()
-
-        try:
-            self.model.predict_generator(
-                evaluation_generator,
-                callbacks=[prediction_manager],
-                verbose=1,
-                steps=len(evaluation_generator))
-        except KeyboardInterrupt:
-            print('\r\nJob interrupted (Ctrl+C): Outputting completed results')
-
-        predictions = prediction_manager.getPredictions()
-
-        # Truncates evaluation generator if test was interrupted
-        evaluation_generator._data = evaluation_generator.data[:predictions.shape[0]]
+        predictions = self.model.predict_generator(
+            evaluation_generator,
+            verbose=1,
+            steps=len(evaluation_generator))
 
         self._save_prediction_histograms(predictions)
         predictions = predictions > threshold
-        ground_truth = evaluation_generator.data
+        ground_truth = self.evaluation_labels[DATA_LABELS].to_numpy()
+        filenames = self.evaluation_labels[["filename"]].to_numpy()
         stats = classification_report(ground_truth, predictions, target_names=DATA_LABELS, output_dict=True)
         stats["total_binary_accuracy"] = 1 - hamming_loss(ground_truth, predictions)
         stats["all_or_nothing_accuracy"] = accuracy_score(ground_truth, predictions)
-        stats["top_10_best"] = self._top_images(evaluation_generator, predictions, best=True)
-        stats["top_10_worst"] = self._top_images(evaluation_generator, predictions, best=False)
+        stats["top_10_best"] = self._top_images(filenames, ground_truth, predictions, best=True)
+        stats["top_10_worst"] = self._top_images(filenames, ground_truth, predictions, best=False)
+        stats["none_of_the_above_recall"] = self._none_of_the_above_recall(ground_truth, predictions)
+        stats["none_of_the_above_precision"] = self._none_of_the_above_precision(ground_truth, predictions)
         return stats
 
-    def _top_images(self, labels, predictions, top_k=10, best=False):
+    def _none_of_the_above_recall(self, labels, predictions):
+        """Returns the recall score for the 'none of the above' images.
+        That means, all the images that don't have a category.
+        """
+        total_count = 0
+        correct_count = 0
+        for item in zip(labels.astype(int), predictions.astype(int)):
+            # Is this a none of the above?
+            if not item[0].any():
+                total_count += 1
+                if not item[1].any():
+                    correct_count += 1
+        if total_count == 0:
+            print("WARNING: None of the Above Recall is NaN")
+            return 0
+        return correct_count / total_count
+
+    def _none_of_the_above_precision(self, labels, predictions):
+        """Returns the precision score for the 'none of the above' images.
+        That means, all the images that don't have a category.
+        """
+        total_count = 0
+        correct_count = 0
+        for item in zip(labels.astype(int), predictions.astype(int)):
+            # Is this a none of the above prediction?
+            if not item[1].any():
+                total_count += 1
+                if not item[0].any():
+                    correct_count += 1
+        if total_count == 0:
+            print("WARNING: None of the Above Precision is NaN")
+            return 0
+        return correct_count / total_count
+
+    def _top_images(self, filenames, ground_truth, predictions, top_k=10, best=False):
         """Collect top-k best or top-k worst predicted images
 
         Keyword arguments:
-        labels -- The keras generator that contains the filenames and data labels
+        ground_truth -- The correct labels
         predictions -- The numpy array of predictions
         top_k -- Top k elements
         best -- True/False. Calculate either the best or worst images
 
         :Return -- Tuple of top-k indicies and top-k filenames
         """
-        true_labels = np.array(labels.data).astype(float)
+        true_labels = np.array(ground_truth).astype(float)
         predictions = predictions.astype(float)
 
         differences = np.abs(predictions - true_labels).sum(axis=1)
@@ -314,7 +362,7 @@ class EyeballModel:
             indicies = np.flipud(indicies)
 
         for i in indicies[:top_k]:
-            top_file_list.append(labels.filenames[i])
+            top_file_list.append(filenames[i][0])
 
         return indicies[:top_k], top_file_list
 

eyeballer/visualization.py

@@ -0,0 +1,129 @@
+import numpy as np
+import math
+import tensorflow as tf
+
+from copy import deepcopy
+from matplotlib import pyplot as plt
+from eyeballer.model import DATA_LABELS
+
+
+class HeatMap():
+    def __init__(self, screenshot_path, model, threshold=0.5, boxsize=28, step=7):
+        self.model = model
+
+        img = tf.keras.preprocessing.image.load_img(screenshot_path, target_size=(self.model.image_width, self.model.image_height))
+        img = tf.keras.preprocessing.image.img_to_array(img)
+
+        self.screenshot = img
+        self.boxsize = boxsize
+        self.step = step
+        self.x = 0
+        self.y = 0
+        self.screenshot_path = screenshot_path
+        self.gamma = 3
+        self.threshold = threshold
+
+    def generate(self, output_file="heatmap.png"):
+        """ Make a single heatmap image and return it """
+        heatmaps = []
+        labels = []
+        results = self.model.predict(self.screenshot_path)
+        results = results[0]
+        for label in DATA_LABELS:
+            boxsize = self.boxsize
+            # Ignore this label if it didn't predict positively (true label)
+            if results[label] > self.threshold:
+                worst_score = 1
+                while worst_score > self.threshold:
+                    heatmap, worst_score = self._get_heatmap(label, boxsize)
+                    if worst_score > self.threshold:
+                        boxsize += 28
+                        print("Didn't get a good image for {}. Trying again with a bigger boxsize: {}".format(label, boxsize))
+                    else:
+                        heatmaps.append(heatmap)
+                        labels.append(label)
+
+        plt.figure()
+        screenshot_image = tf.keras.preprocessing.image.load_img(self.screenshot_path, target_size=(self.model.image_width, self.model.image_width))
+        _, subplots = plt.subplots(1, len(heatmaps))
+        # This is a little janky, but matplotlib returns a list above if there's multiple subplots, and just a single subplot if there's only one
+        #   it would have been easier if it was always a list. But alas
+        if not heatmaps:
+            print("No heatmap made. The image did not have a True classification for any label")
+            return
+        if len(heatmaps) > 1:
+            for i, heatmap in enumerate(heatmaps):
+                subplots[i].imshow(screenshot_image, cmap='binary', interpolation='none')
+                subplots[i].imshow(heatmap, alpha=0.5, interpolation='none')
+                subplots[i].set_title(labels[i])
+        else:
+            subplots.imshow(screenshot_image, cmap='binary', interpolation='none')
+            subplots.imshow(heatmap, alpha=0.5, interpolation='none')
+            subplots.set_title(labels[0])
+        plt.savefig(output_file)
+        print("Heatmap image written to: {}".format(output_file))
+
+    def _get_heatmap(self, label, boxsize):
+        worst_score = 1
+        self.x, self.y = 0, 0
+
+        heatmap = np.ones((self.model.image_width, self.model.image_height))
+        heatmap *= 255
+        while True:
+            # Occlude an image
+            occluded_image, x, y = self._occlude(boxsize)
+            if occluded_image is not None:
+                # Score the new occluded image
+                results = self.model.predict_on_array(occluded_image)
+                score = results[label]  # TODO
+                worst_score = min(worst_score, score)
+                # Scale the score up to pixel values
+                score = math.floor(score * 255)
+                # Anneal the scores as you go outward into the occlusion block
+                new_scores = np.ones((boxsize, boxsize))
+                new_scores *= score
+                new_scores = self._gamma_anneal(new_scores)
+
+                # For each pixel, update only where the new score is lower
+                original_scores = heatmap[x: x+boxsize, y: y+boxsize]
+
+                target_area = np.where(new_scores < original_scores, new_scores, original_scores)
+                heatmap[x: x+boxsize, y: y+boxsize] = target_area
+            else:
+                break
+
+        return heatmap, worst_score
+
+    def _gamma_anneal(self, occlusion_area):
+        """ Get the annealed score based on centeredness of the occlusion area """
+        width, height = occlusion_area.shape
+
+        center_x = (width-1) / 2
+        center_y = (height-1) / 2
+
+        annealed_area = np.zeros(occlusion_area.shape)
+
+        for (x, y), score in np.ndenumerate(occlusion_area):
+            distance = math.sqrt((center_x - x)**2 + (center_y - y)**2)
+            annealed_area[x, y] = min(255, score + (self.gamma * distance))
+
+        return annealed_area
+
+    def _occlude(self, boxsize):
+        ''' Return a single occluded image and its location '''
+        if self.x + boxsize > self.screenshot.shape[0]:
+            return None, None, None
+
+        retImg = np.copy(self.screenshot)
+        retImg[self.x:self.x+boxsize, self.y:self.y+boxsize] = 0.0
+
+        old_i = deepcopy(self.x)
+        old_j = deepcopy(self.y)
+
+        # update indices
+        self.y = self.y + self.step
+        if self.y+boxsize > self.screenshot.shape[1]:  # reached end
+            self.y = 0  # reset j
+            self.x = self.x + self.step  # go to next row
+
+        return retImg, old_i, old_j

prediction_output_template.html

@@ -0,0 +1,189 @@
+<!DOCTYPE HTML>
+<meta charset="utf-8"/>
+<html>
+<title>Eyeballer Results</title>
+<body>
+    <div id="btnsCont">
+      <button class="btn all" onclick="filterSelection('all')"> Show all</button>
+      <button class="btn" onclick="filterSelection('custom404')"> 404</button>
+      <button class="btn" onclick="filterSelection('login')"> Login</button>
+      <button class="btn" onclick="filterSelection('homepage')"> Home Page</button>
+      <button class="btn" onclick="filterSelection('oldlooking')"> Old Looking</button>
+        <select onchange="numOnPage=this.value; filterSelection([]);" style="float:right; margin:5px; margin-right:30px; height:30px; width:auto;">
+          <option value="25">25</option>
+          <option value="50">50</option>
+          <option value="100">100</option>
+          <option value="250">250</option>
+        </select>
+    </div>
+    <div id="btnsPage">
+    </div>
+    <div id="container">
+    </div>
+    <button onclick="topFunction()" id="topBtn" title="Go to top">Top</button>
+<script>
+    var results = {{jsResults}};
+    var selectedTags = new Array();
+    var btnContainer = document.getElementById("btnsCont");
+    var categoryBtns = btnContainer.getElementsByClassName("btn");
+    var numOnPage = 100;
+    var indexesToDisplay = [];
+    filterSelection([]);
+    for (var i = 0; i < categoryBtns.length; i++) {
+        categoryBtns[i].addEventListener("click", function () {
+            if (this.className.indexOf("active") > -1) {
+                this.className = this.className.replace(" active", "");
+            }
+            else {
+            this.className += " active";
+            }
+        });
+    }
+    function filterSelection(c) {
+        indexesToDisplay = [];
+        var currentIndex = -1;
+        if (c == "all") location.reload();
+        if (selectedTags.includes(c)!=true) selectedTags=selectedTags.concat(c);
+        else selectedTags.splice(selectedTags.indexOf(c),1);
+        for (var filename in results){
+                currentIndex++;
+                var tags = results[filename];
+                var isMissingTag = false;
+                for (var i in selectedTags){
+                    if (!tags.includes(selectedTags[i])){
+                        isMissingTag = true;
+                        break;
+                    }
+                }
+                if (isMissingTag === false){
+                    indexesToDisplay = indexesToDisplay.concat(currentIndex);
+                }
+            }
+        buildPageButtons(indexesToDisplay);
+        buildHTML(0, numOnPage);
+    }
+    function buildPageButtons(indexes){
+        numOfPages = Math.ceil(indexes.length/numOnPage);
+        var html = '';
+        for (var i = 0; i < numOfPages; i++){
+            html += ('<button class="btnpage" onclick="buildHTML(' + (i*numOnPage) + ', ' + ((i+1)*numOnPage) + '); activatePage(this);">' + (i+1) +'</button>');
+        }
+        document.getElementById('btnsPage').innerHTML = html;
+    }
+    function activatePage(pressedBtn){
+        var activeNow = document.getElementsByClassName('activePage')
+        for (var i = 0; i < activeNow.length; i++){
+            activeNow[i].className = activeNow[i].className.replace(" activePage", "");
+        }
+        pressedBtn.className += ' activePage';
+    }
+    function buildHTML(rangeMin, rangeMax){
+        var html = '';
+        if (rangeMax > indexesToDisplay.length) rangeMax=indexesToDisplay.length;
+        for (var i = rangeMin; i < rangeMax; i++){
+            index=indexesToDisplay[i];
+            filename = Object.keys(results)[index];
+            html += ('<div class="filterDiv"><a href=' + filename + ' target="_blank"> ' + results[filename].toString().replace(/,/g, ' ') + '</a><img class="res_pic" src="' + filename + '"></div>');
+        }
+        document.getElementById('container').innerHTML = html;
+    }
+    window.onscroll = function() {scrollFunction()};
+    function scrollFunction() {
+      if (document.body.scrollTop > 20 || document.documentElement.scrollTop > 20) {
+        document.getElementById("topBtn").style.display = "block";
+      } else {
+        document.getElementById("topBtn").style.display = "none";
+      }
+    }
+    function topFunction() {
+      document.body.scrollTop = 0; // For Safari
+      document.documentElement.scrollTop = 0; // For Chrome, Firefox, IE and Opera
+    }
+</script>
+    <style>
+    #topBtn {
+      display: none; /* Hidden by default */
+      position: fixed; /* Fixed/sticky position */
+      bottom: 20px; /* Place the button at the bottom of the page */
+      right: 30px; /* Place the button 30px from the right */
+      z-index: 99; /* Make sure it does not overlap */
+      border: none; /* Remove borders */
+      outline: none; /* Remove outline */
+      background-color: #193b72; /* Set a background color */
+      color: white; /* Text color */
+      cursor: pointer; /* Add a mouse pointer on hover */
+      padding: 15px; /* Some padding */
+      border-radius: 10px; /* Rounded corners */
+      font-size: 18px; /* Increase font size */
+      font-weight:bold;
+      border:solid;
+    }
+    #topBtn:hover {
+      background-color: #555;
+    }
+    .container {
+    overflow: hidden;
+    }
+    #btnsPage {
+    margin:10px;
+    }
+    .btnpage{
+        width:40px;
+        height:30px;
+        background-color: #f1f1f1;
+        outline: none;
+    }
+    .res_pic{
+    width:100%;
+    height:auto;
+    }
+    a{
+    font-weight:600;
+    padding:3px;
+    text-decoration: none;
+    color:#ffffff;
+    font-size:16px;
+    font-family:Sans-serif;
+    }
+    .name{
+    float:left;
+    }
+    .filterDiv {
+      float: left;
+      background-color: #777;
+      color: #ffffff;
+      width: 22%;
+      border-style:solid;
+      border-color:#666;
+      text-align: center;
+      margin: 10px;
+      line-height: 30px;
+      display: block;
+    }
+    .btn {
+      border: none;
+      outline: none;
+      padding: 12px 16px;
+      margin-left:10px;
+      background-color: #f1f1f1;
+      cursor: pointer;
+    }
+    .btn:hover {
+      background-color: #ddd;
+    }
+    .active {
+      background-color: #666;
+      color: white;
+    }
+    .activePage{
+        border: solid;
+        border-color:#666666;
+        color: black;
+    }
+    .btn.all {
+      background-color: #193b72;
+      color: white;
+    }
+    </style>
+</body>
+</html>

requirements-gpu.txt

@@ -0,0 +1,10 @@
+Augmentor
+click
+matplotlib
+numpy
+pandas
+pillow
+sklearn
+tensorflow-gpu
+jinja2
+progressbar2

requirements.txt

@@ -6,4 +6,5 @@ pandas
 pillow
 sklearn
 tensorflow
-git+https://github.com/keras-team/keras/
+jinja2
+progressbar2

tests/PredictTest.py

@@ -0,0 +1,142 @@
+import errno
+import os
+import unittest
+import sys
+
+from eyeballer.model import EyeballModel
+from eyeballer.visualization import HeatMap
+
+
+class PredictTest(unittest.TestCase):
+    def setUp(self):
+
+        class DummyFile(object):
+            def write(self, x): pass
+            def flush(self): pass
+
+        sys.stdout = DummyFile()
+        model_file = "tests/models/model_file.h5"
+        if not os.path.isfile(model_file):
+            print("Error: Symlink the latest model file to " + model_file)
+            raise FileNotFoundError(
+                errno.ENOENT,
+                os.strerror(errno.ENOENT),
+                model_file)
+
+        model_kwargs = {
+            "model_file": model_file,
+            "print_summary": False,
+            "seed": None,
+            "quiet": True
+        }
+        self.model = EyeballModel(**model_kwargs)
+
+    def test_different_seed_predict(self):
+        model_kwargs = {
+            "model_file": None,
+            "print_summary": False,
+            "seed": 12345678,
+            "quiet": True
+        }
+        same_seed_model = EyeballModel(**model_kwargs)
+
+        screenshot = "tests/data/404.png"
+
+        results_one = self.model.predict(screenshot)
+        results_two = same_seed_model.predict(screenshot)
+
+        self.assertNotEqual(results_one, results_two)
+
+    def test_same_seed_predict(self):
+        model_kwargs = {
+            "model_file": None,
+            "print_summary": False,
+            "seed": 12345678,
+            "quiet": True
+        }
+        same_seed_model = EyeballModel(**model_kwargs)
+
+        screenshot = "tests/data/404.png"
+
+        results_one = same_seed_model.predict(screenshot)
+        results_two = same_seed_model.predict(screenshot)
+
+        self.assertEqual(results_one, results_two)
+
+    def test_predict_custom404(self):
+        screenshot = "tests/data/404.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertGreater(results["custom404"], 0.5)
+
+    def test_predict_not_custom404(self):
+        screenshot = "tests/data/nothing.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertLess(results["custom404"], 0.5)
+
+    def test_predict_login(self):
+        screenshot = "tests/data/login.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertGreater(results["login"], 0.5)
+
+    def test_predict_not_login(self):
+        screenshot = "tests/data/nothing.png"
+        results = self.model.predict(screenshot)[0]
+        print(screenshot, results)
+        self.assertLess(results["login"], 0.5)
+
+    def test_predict_homepage(self):
+        screenshot = "tests/data/homepage.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertGreater(results["homepage"], 0.5)
+
+    def test_predict_not_homepage(self):
+        screenshot = "tests/data/nothing.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertLess(results["homepage"], 0.5)
+
+    def test_predict_oldlooking(self):
+        screenshot = "tests/data/old-looking.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertGreater(results["oldlooking"], 0.5)
+
+    def test_predict_not_oldlooking(self):
+        screenshot = "tests/data/nothing.png"
+        results = self.model.predict(screenshot)[0]
+        self.assertLess(results["oldlooking"], 0.5)
+
+    def test_file_doesnt_exist(self):
+        screenshot = "tests/data/doesnotexist.png"
+        try:
+            self.model.predict(screenshot)[0]
+            self.fail("FileNotFoundError was expected but not found")
+        except FileNotFoundError:
+            pass
+
+    def test_folder(self):
+        screenshots = "tests/data/"
+        results = self.model.predict(screenshots)
+        self.assertEqual(len(results), 7)
+
+    def test_file_is_empty(self):
+        """
+        We're just testing that it doesn't crash, basically
+        """
+        screenshot = "tests/data/empty.png"
+        self.model.predict(screenshot)
+
+    def test_file_is_invalid(self):
+        """
+        We're just testing that it doesn't crash, basically
+        """
+        screenshot = "tests/data/invalid.png"
+        self.model.predict(screenshot)
+
+    def test_heatmap(self):
+        screenshot = "tests/data/login.png"
+        HeatMap(screenshot, self.model, 0.5)
+
+        screenshot = "tests/data/404.png"
+        HeatMap(screenshot, self.model, 0.5)
+
+        screenshot = "tests/data/empty.png"
+        HeatMap(screenshot, self.model, 0.5)

tests/__init__.py

@@ -0,0 +1 @@
+from tests.PredictTest import *  # noqa

tests/data/invalid.png

@@ -0,0 +1 @@
+THIS TEXT IS NOT A VALID PNG

webui/.gitignore

@@ -0,0 +1,20 @@
+# See https://help.github.com/ignore-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+
+# testing
+/coverage
+
+# production
+/build
+
+# misc
+.DS_Store
+.env
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+#Model files
+public/jsmodel/*

webui/package.json

@@ -0,0 +1,20 @@
+{
+  "name": "eyeballer-webui",
+  "version": "0.1.0",
+  "private": true,
+  "dependencies": {
+    "react": "^16.11.0",
+    "react-dom": "^16.11.0",
+    "react-dropzone": "^10.1.10",
+    "react-scripts": "0.9.5",
+    "bootstrap": "^4.3.1",
+    "@tensorflow/tfjs": "^1.3.2"
+  },
+  "devDependencies": {},
+  "scripts": {
+    "start": "react-scripts start",
+    "build": "react-scripts build",
+    "test": "react-scripts test --env=jsdom",
+    "eject": "react-scripts eject"
+  }
+}

webui/public/index.html

@@ -0,0 +1,31 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <link rel="shortcut icon" href="%PUBLIC_URL%/favicon.png">
+    <!--
+      Notice the use of %PUBLIC_URL% in the tag above.
+      It will be replaced with the URL of the `public` folder during the build.
+      Only files inside the `public` folder can be referenced from the HTML.
+
+      Unlike "/favicon.ico" or "favicon.ico", "%PUBLIC_URL%/favicon.ico" will
+      work correctly both with client-side routing and a non-root public URL.
+      Learn how to configure a non-root public URL by running `npm run build`.
+    -->
+    <title>React App</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <!--
+      This HTML file is a template.
+      If you open it directly in the browser, you will see an empty page.
+
+      You can add webfonts, meta tags, or analytics to this file.
+      The build step will place the bundled scripts into the <body> tag.
+
+      To begin the development, run `npm start`.
+      To create a production bundle, use `npm run build`.
+    -->
+  </body>
+</html>

webui/src/App.css

@@ -0,0 +1,23 @@
+.App {
+  text-align: center;
+}
+
+.App-logo {
+  height: 100px;
+}
+
+.App-header {
+  background-color: #222;
+  height: 170px;
+  padding: 10px;
+  color: white;
+}
+
+.App-intro {
+  font-size: large;
+}
+
+.thumbnail {
+  height: 280px;
+  width: 480px;
+}

webui/src/App.js

@@ -0,0 +1,132 @@
+import React, { Component } from 'react';
+import logo from './eyeballer_logo.png';
+import './App.css';
+import 'bootstrap/dist/css/bootstrap.min.css';
+import * as tf from '@tensorflow/tfjs';
+
+class App extends Component {
+  constructor(props) {
+    super(props);
+    this.state = {
+      fileContents: "Try uploading a file",
+    }
+  }
+
+  render() {
+    return (
+      <div className="App">
+        <div className="App-header">
+          <img src={logo} className="App-logo" alt="logo" />
+          <h2>Eyeballer</h2>
+        </div>
+        <PredictButton />
+        <ImageFile />
+
+      </div>
+    );
+  }
+}
+
+export default App;
+
+class ImageFile extends React.Component {
+  constructor(props){
+    super(props);
+    this.state = {
+      id : "someUniqueId", // I would use this.props.id for a real world implementation
+      imageURI : null
+    }
+  }
+
+  buildImgTag(){
+    let imgTag = null;
+    if (this.state.imageURI !== null)
+      imgTag = (<div className="row">
+                  <div className="small-9 small-centered columns">
+                    <img className="thumbnail" id="thumbnail" alt="Screenshot to be classified" src={this.state.imageURI}></img>
+                  </div>
+                </div>);
+    return imgTag;
+  }
+
+  readURI(e){
+    if(e.target.files && e.target.files[0]){
+      let reader = new FileReader();
+      reader.onload = function(ev){
+        this.setState({imageURI:ev.target.result});
+      }.bind(this);
+      reader.readAsDataURL(e.target.files[0]);
+    }
+  }
+
+  handleChange(e){
+    this.readURI(e); // maybe call this with webworker or async library?
+    if (this.props.onChange !== undefined)
+      this.props.onChange(e); // propagate to parent component
+  }
+
+  render() {
+    const imgTag = this.buildImgTag();
+
+    return <div>
+            <label
+              htmlFor={this.state.id}
+              className="button">
+              Upload an image
+            </label>
+            <input
+              id={this.state.id}
+              type="file"
+              onChange={this.handleChange.bind(this)}
+              className="show-for-sr" />
+            {imgTag}
+          </div>;
+  }
+}
+
+class PredictButton extends Component {
+  constructor(props) {
+    super(props);
+    this.state = {
+      classification: ""
+    }
+  }
+
+  onpress = async () => {
+    const modelUrl = 'jsmodel/model.json';
+    const model = await tf.loadLayersModel(modelUrl);
+    const offset = tf.scalar(127.5);
+    const image = tf.browser.fromPixels(document.getElementById('thumbnail'))
+      .resizeNearestNeighbor([224,224])
+      .toFloat()
+      .sub(offset)
+      .div(offset)
+      .expandDims();
+
+    const predictions = Array.from(model.predict(image).dataSync());
+    this.setState({classification: ""});
+    if (predictions[0] > 0.5) {
+      this.setState({classification: this.state.classification + "Custom 404, "});
+    }
+    if (predictions[1] > 0.5) {
+      this.setState({classification: this.state.classification + "Login Page, "});
+    }
+    if (predictions[2] > 0.5) {
+      this.setState({classification: this.state.classification + "Homepage, "});
+    }
+    if (predictions[3] > 0.5) {
+      this.setState({classification: this.state.classification + "Old Looking "});
+    }
+
+  }
+
+  render() {
+    return (
+      <div className="custom-file">
+        <button className="default" type="button" id="predictButton" onClick={this.onpress}>Classify</button>
+        {this.state.classification}
+      </div>
+    );
+  }
+
+}

webui/src/App.test.js

@@ -0,0 +1,8 @@
+import React from 'react';
+import ReactDOM from 'react-dom';
+import App from './App';
+
+it('renders without crashing', () => {
+  const div = document.createElement('div');
+  ReactDOM.render(<App />, div);
+});

webui/src/index.css

@@ -0,0 +1,5 @@
+body {
+  margin: 0;
+  padding: 0;
+  font-family: sans-serif;
+}

webui/src/index.js

@@ -0,0 +1,9 @@
+import React from 'react';
+import ReactDOM from 'react-dom';
+import App from './App';
+import './index.css';
+
+ReactDOM.render(
+  <App />,
+  document.getElementById('root')
+);