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Efficiently Constructing Adversarial Examples by Feature Watermarking | | Yuexin Xiang
; Wei Ren
; Tiantian Li
; Xianghan Zheng
; Tianqing Zhu
; Kim-Kwang Raymond Choo
; | | Date: |
14 Aug 2020 | | Abstract: | With the increasing attentions of deep learning 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 deep learning 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 adversarial examples for deep
learning models. Specifically, partial main features of the watermark image are
embedded into the host image invisibly, aiming to tamper and damage the
recognition capabilities of the deep learning 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 and the
modified discrete cosine transform (DCT) based Patchwork watermarking
algorithm. The experimental results showed that our scheme is able to generate
a large number of adversarial examples efficiently. In addition, we find that
using the extracted features of the image as the watermark images, can increase
the success rate of an attack under certain conditions with minimal changes to
the host image. To ensure repeatability, reproducibility, and code sharing, the
source code is available on GitHub | | Source: | arXiv, 2009.05107 | | Services: | Forum | Review | PDF | Favorites | |
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