IU: Imperceptible Universal Backdoor Attack
Backdoor attacks pose a critical threat to the security of deep neural networks, yet existing efforts on universal backdoors often rely on visually salient patterns, making them easier to detect and less practical at scale. In this work, we introduce a novel imperceptible universal backdoor attack that simultaneously controls all target classes with minimal poisoning while preserving stealth. Our key idea is to leverage graph convolutional networks (GCNs) to model inter-class relationships and generate class-specific perturbations that are both effective and visually invisible. The proposed framework optimizes a dual-objective loss that balances stealthiness (measured by perceptual simila...
| Authors | Hsin Lin, Yan-Lun Chen, Ren-Hung Hwang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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