With False Friends Like These, Who Can Have Self-Knowledge?
Year: 2021
Type: preprint
Abstract: Adversarial examples arise from excessive sensitivity of a model. Commonly studied adversarial examples are malicious inputs, crafted by an adversary from correctly classified examples, to induce misclassification. This paper studies an intriguing, yet far overlooked consequence of the excessive sensitivity, that is, a misclassified example can be easily perturbed to help the model to produce correct output. Such perturbed examples look harmless, but actually can be maliciously utilized by a false friend to make the model self-satisfied. Thus we name them hypocritical examples. With false friends like these, a poorly performed model could behave like a state-of-the-art one. Once a deployer trusts the hypocritical performance and uses the well-performed model in real-world applications, potential security concerns appear even in benign environments. In this paper, we formalize the hypocritical risk for the first time and propose a defense method specialized for hypocritical examples by minimizing the tradeoff between natural risk and an upper bound of hypocritical risk. Moreover, our theoretical analysis reveals connections between adversarial risk and hypocritical risk. Extensive experiments verify the theoretical results and the effectiveness of our proposed methods.
Source: arXiv (Cornell University)
Authors: Lue Tao, Songcan Chen
Institution: Nanjing University of Aeronautics and Astronautics
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3114857898