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Poster
Class-Disentanglement and Applications in Adversarial Detection and Defense
Kaiwen Yang · Tianyi Zhou · Yonggang Zhang · Xinmei Tian · Dacheng Tao

Wed Dec 08 04:30 PM -- 06:00 PM (PST) @ Virtual
What is the minimum necessary information required by a neural net $D(\cdot)$ from an image $x$ to accurately predict its class? Extracting such information in the input space from $x$ can allocate the areas $D(\cdot)$ mainly attending to and shed novel insights to the detection and defense of adversarial attacks. In this paper, we propose ''class-disentanglement'' that trains a variational autoencoder $G(\cdot)$ to extract this class-dependent information as $x - G(x)$ via a trade-off between reconstructing $x$ by $G(x)$ and classifying $x$ by $D(x-G(x))$, where the former competes with the latter in decomposing $x$ so the latter retains only necessary information for classification in $x-G(x)$. We apply it to both clean images and their adversarial images and discover that the perturbations generated by adversarial attacks mainly lie in the class-dependent part $x-G(x)$. The decomposition results also provide novel interpretations to classification and attack models. Inspired by these observations, we propose to conduct adversarial detection and adversarial defense respectively on $x - G(x)$ and $G(x)$, which consistently outperform the results on the original $x$. In experiments, this simple approach substantially improves the detection and defense against different types of adversarial attacks.

#### Author Information

##### Tianyi Zhou (University of Washington, Seattle)

Tianyi Zhou (https://tianyizhou.github.io) is a tenure-track assistant professor of computer science at the University of Maryland, College Park. He received his Ph.D. from the school of computer science & engineering at the University of Washington, Seattle. His research interests are in machine learning, optimization, and natural language processing (NLP). His recent works study curriculum learning that can combine high-level human learning strategies with model training dynamics to create a hybrid intelligence. The applications include semi/self-supervised learning, robust learning, reinforcement learning, meta-learning, ensemble learning, etc. He published >80 papers and is a recipient of the Best Student Paper Award at ICDM 2013 and the 2020 IEEE Computer Society TCSC Most Influential Paper Award.