[2603.00711] IU: Imperceptible Universal Backdoor Attack

Computer Science > Cryptography and Security arXiv:2603.00711 (cs) [Submitted on 28 Feb 2026] Title:IU: Imperceptible Universal Backdoor Attack Authors:Hsin Lin, Yan-Lun Chen, Ren-Hung Hwang, Chia-Mu Yu View a PDF of the paper titled IU: Imperceptible Universal Backdoor Attack, by Hsin Lin and 3 other authors View PDF HTML (experimental) Abstract: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 similarity metrics such as PSNR) and attack success rate (ASR), enabling scalable, multi-target backdoor injection. Extensive experiments on ImageNet-1K with ResNet architectures demonstrate that our method achieves high ASR (up to 91.3%) under poisoning rates as low as 0.16%, while maintaining benign accuracy and evading state-of-the-art defenses. These results highlight the emerging risks of invisible universal backdoors and call for more robust detec...