  Convolutional Neural Networks (CNNs) deployed in real-life applications such
as autonomous vehicles have shown to be vulnerable to manipulation attacks,
such as poisoning attacks and fine-tuning. Hence, it is essential to ensure the
integrity and authenticity of CNNs because compromised models can produce
incorrect outputs and behave maliciously. In this paper, we propose a
self-contained tamper-proofing method, called DeepiSign, to ensure the
integrity and authenticity of CNN models against such manipulation attacks.
DeepiSign applies the idea of fragile invisible watermarking to securely embed
a secret and its hash value into a CNN model. To verify the integrity and
authenticity of the model, we retrieve the secret from the model, compute the
hash value of the secret, and compare it with the embedded hash value. To
minimize the effects of the embedded secret on the CNN model, we use a
wavelet-based technique to transform weights into the frequency domain and
embed the secret into less significant coefficients. Our theoretical analysis
shows that DeepiSign can hide up to 1KB secret in each layer with minimal loss
of the model's accuracy. To evaluate the security and performance of DeepiSign,
we performed experiments on four pre-trained models (ResNet18, VGG16, AlexNet,
and MobileNet) using three datasets (MNIST, CIFAR-10, and Imagenet) against
three types of manipulation attacks (targeted input poisoning, output
poisoning, and fine-tuning). The results demonstrate that DeepiSign is
verifiable without degrading the classification accuracy, and robust against
representative CNN manipulation attacks.
