Dazhuang Liu (Delft University of Technology), Yanqi Qiao (Delft University of Technology), Rui Wang (Delft University of Technology), Kaitai Liang (Delft University of Technology), Georgios Smaragdakis (Delft University of Technology)
Current black-box backdoor attacks in convolutional neural networks formulate attack objective(s) as textit{single-objective} optimization problems in textit{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 MOEA to exclude impractical triggers.
LADDER investigates a new dual-domain perspective for trigger stealthiness by minimizing the anomaly between clean and poisoned samples in the spectral domain.
Lastly, the robustness against preprocessing operations is achieved by pushing triggers to low-frequency regions.
Extensive experiments comprehensively showcase that LADDER achieves attack effectiveness of at least 99%, attack robustness with 90.23% (50.09% higher than state-of-the-art attacks on average), superior natural stealthiness (1.12$times$ to 196.74$times$ improvement) and excellent spectral stealthiness (8.45$times$ enhancement) as compared to current stealthy attacks by the average $l_2$-norm across 5 public datasets.