def test_cifar10():
data_train, data_test = __prepare_datasets()
model = __prepare_model(data_train, data_test)
print("---Evaluation---")
loss, acc = model.evaluate(*data_test)
print(f"loss={loss}, acc={acc}")
print("---Fault Injection---")
pixels = np.array(
[
PixelFault(5, 5, 0, 0, 0),
PixelFault(6, 6, 0, 0, 0),
PixelFault(7, 7, 0, 0, 0),
],
dtype=object,
)
faulted_model = InputTensorFI.build_faulted_model(
model,
fi_layers=[
PixelFiLayerTF(pixels, dtype=tf.uint8),
],
)
print("---Evaluation with FI---")
logdir = "logs/compile/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)
loss, acc = faulted_model.evaluate(
*data_test, callbacks=[tensorboard_callback]
)
print(f"loss={loss}, acc={acc}")
def test_perturb_image():
pixels = np.array(
[PixelFault(16, 16, 255, 255, 0),
PixelFault(5, 5, 255, 255, 0)],
dtype=object,
)
(_, _), (x_test, _) = cifar10.load_data()
image = x_test[0]
perturb_image = utils.build_perturb_image(pixels)
images_pertubed = perturb_image(image)
assert np.array_equal(images_pertubed[16, 16], np.array((255, 255, 0)))
assert np.array_equal(images_pertubed[5, 5], np.array((255, 255, 0)))
def test_cifar10_one():
data_train, data_test = __prepare_datasets()
model = __prepare_model(data_train, data_test)
print("---Evaluation---")
image_id = 541
x_test, y_test = data_test
x = x_test[image_id]
y_true = y_test[image_id]
y_true_index = np.argmax(y_true)
result = model.predict(np.array([x]))[0]
result_index = np.argmax(result)
print(f"result={result}")
print(f"result_index={result_index}")
print(f"y_true={y_true}")
print(f"y_true_index={y_true_index}")
print(f"result[y_true_index]={result[y_true_index]}")
print("---Fault Injection---")
pixels = np.array(
[
PixelFault(5, 5, 0, 0, 0),
PixelFault(6, 6, 0, 0, 0),
PixelFault(7, 7, 0, 0, 0),
],
dtype=object,
)
faulted_model = InputTensorFI.build_faulted_model(
model,
fi_layers=[
PixelFiLayerTF(pixels, dtype=tf.uint8),
],
)
print("---Evaluation with FI---")
result = faulted_model.predict(np.array([x_test[image_id]]))[0]
result_index = np.argmax(result)
print(f"result={result}")
print(f"result_index={result_index}")
print(f"y_true={y_true}")
print(f"y_true_index={y_true_index}")
print(f"result[y_true_index]={result[y_true_index]}")
# Plot
perturbated_image = build_perturb_image(pixels)(x_test[image_id])
utils.plot_image(perturbated_image)
def corner_search(
image_id: int,
pixels: np.ndarray,
data_test: np.ndarray,
model: tf.keras.Model,
) -> Iterable[Tuple[np.ndarray, np.ndarray, PixelFault]]:
x_test, y_test = data_test
y_true = y_test[image_id]
y_true_index = np.argmax(y_true)
for pixel in pixels:
corner_pixels = [
PixelFault(pixel.x, pixel.y, r, g, b) for r, g, b in CORNERS
]
x_fakes = np.array([
build_perturb_image([corner_pixel])(x_test[image_id])
for corner_pixel in corner_pixels
])
y_preds = model.predict(x_fakes)
for x_fake, y_pred, corner_pixel in zip(
x_fakes,
y_preds,
corner_pixels,
):
y_pred_index = np.argmax(y_pred)
if y_true_index != y_pred_index:
yield x_fake, y_pred, corner_pixel
def test_PixelFiLayer():
pixels = np.array(
[PixelFault(16, 16, 255, 255, 0),
PixelFault(5, 5, 255, 255, 0)],
dtype=object,
)
(_, _), (x_test, _) = cifar10.load_data()
image = x_test[0]
model = tf.keras.Sequential(layers=[
tf.keras.layers.Input(dtype=tf.uint8, shape=(32, 32, 3)),
layers.PixelFiLayer(pixels),
])
image_perturbed: np.ndarray = model.predict(np.array([image]))[0]
assert np.array_equal(image_perturbed[16, 16], np.array((255, 255, 0)))
assert np.array_equal(image_perturbed[5, 5], np.array((255, 255, 0)))
def _look_for_pixels(
image_id: int,
data_test: np.ndarray,
model: tf.keras.Model,
):
x_test, y_test = data_test
x = x_test[image_id]
y_true = y_test[image_id]
y_true_index = np.argmax(y_true)
pixels = attack(
x,
y_true_index,
model,
pixel_count=1, # Number of pixels to attack
verbose=True,
).astype(np.uint8)
# Convert [x_0, y_0, r_0, g_0, b_0, x_1, ...]
# to [pixel_fault_0, pixel_fault_1, ...]
return np.array([PixelFault(*pixels[0:5]) for i in range(len(pixels) // 5)])