  With the increasing attention to deep neural network (DNN) models, attacks
are also upcoming for such models. For example, an attacker may carefully
construct images in specific ways (also referred to as adversarial examples)
aiming to mislead the DNN models to output incorrect classification results.
Similarly, many efforts are proposed to detect and mitigate adversarial
examples, usually for certain dedicated attacks. In this paper, we propose a
novel digital watermark-based method to generate image adversarial examples to
fool DNN models. Specifically, partial main features of the watermark image are
embedded into the host image almost invisibly, aiming to tamper with and damage
the recognition capabilities of the DNN models. We devise an efficient
mechanism to select host images and watermark images and utilize the improved
discrete wavelet transform (DWT) based Patchwork watermarking algorithm with a
set of valid hyperparameters to embed digital watermarks from the watermark
image dataset into original images for generating image adversarial examples.
The experimental results illustrate that the attack success rate on common DNN
models can reach an average of 95.47% on the CIFAR-10 dataset and the highest
at 98.71%. Besides, our scheme is able to generate a large number of
adversarial examples efficiently, concretely, an average of 1.17 seconds for
completing the attacks on each image on the CIFAR-10 dataset. In addition, we
design a baseline experiment using the watermark images generated by Gaussian
noise as the watermark image dataset that also displays the effectiveness of
our scheme. Similarly, we also propose the modified discrete cosine transform
(DCT) based Patchwork watermarking algorithm. To ensure repeatability and
reproducibility, the source code is available on GitHub.
