TROJFAIR: TROJAN FAIRNESS ATTACKS
Primary Area: societal considerations including fairness, safety, privacy
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Keywords: Deep Learning, Trojan Fairness Attacks
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TL;DR: TrojFair presents an innovative and covert Trojan Fairness attack method for deep learning models.
Abstract: Deep learning models have been incorporated into high-stakes sectors, including healthcare diagnosis, loan approvals, and candidate recruitment, among others. Consequently, any bias or unfairness in these models can harm those who depend on such models. In response, many algorithms have emerged to ensure fairness in deep learning. However, while the potential for harm is substantial, the resilience of these fair deep learning models against malicious attacks has never been thoroughly explored, especially in the context of emerging Trojan attacks. Moving beyond prior research, we aim to fill this void by introducing \textit{TrojFair}, a Trojan fairness attack. Unlike existing attacks, TrojFair is model-agnostic and crafts a Trojaned model that functions accurately and equitably for clean inputs. However, it displays discriminatory behaviors - producing both incorrect and unfair results - for specific groups with tainted inputs containing a trigger. TrojFair is a stealthy Fairness attack that is resilient to existing model fairness audition detectors since the model for clean inputs is fair. TrojFair achieves a target group attack success rate exceeding 88.77\%, with an average accuracy loss less than 0.44\%. It also maintains a high discriminative score between the target and untarget groups across various datasets and models.
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Submission Number: 8320
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