LADDER: Multi-objective Backdoor Attack via Evolutionary Algorithm
Dazhuang Liu, Yanqi Qiao, Rui Wang, Kaitai Liang, Georgios Smaragdakis

TL;DR
LADDER introduces a multi-objective evolutionary algorithm for black-box backdoor attacks on CNNs, optimizing triggers across dual domains to enhance stealthiness, robustness, and attack success rate without prior model knowledge.
Contribution
It is the first to formulate and solve a multi-objective backdoor attack in dual domains using evolutionary algorithms, improving stealthiness and robustness.
Findings
Achieves at least 99% attack effectiveness.
Improves robustness by 90.23%, surpassing state-of-the-art.
Significantly enhances spectral and natural stealthiness.
Abstract
Current black-box backdoor attacks in convolutional neural networks formulate attack objective(s) as single-objective optimization problems in single domain. Designing triggers in single domain harms semantics and trigger robustness as well as introduces visual and spectral anomaly. This work proposes a multi-objective black-box backdoor attack in dual domains via evolutionary algorithm (LADDER), the first instance of achieving multiple attack objectives simultaneously by optimizing triggers without requiring prior knowledge about victim model. In particular, we formulate LADDER as a multi-objective optimization problem (MOP) and solve it via multi-objective evolutionary algorithm (MOEA). MOEA maintains a population of triggers with trade-offs among attack objectives and uses non-dominated sort to drive triggers toward optimal solutions. We further apply preference-based selection to…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Advanced Malware Detection Techniques · Smart Grid Security and Resilience
