# Imperceptible Sample-Specific Backdoor to DNN with Denoising Autoencoder Source: [Imperceptible Sample-Specific Backdoor to DNN with Denoising Autoencoder]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:51:28.908Z Authors: Xiangqi Wang, Mingfu Xue, Kewei Chen, Jing Xu, Wenmao Liu, Leo Yu Zhang, Yushu Zhang Published: 2023-02-09T06:33:15.000Z ## Abstract The backdoor attack poses a new security threat to deep neural networks. Existing backdoor often relies on visible universal trigger to make the backdoored model malfunction, which are not only usually visually suspicious to human but also catchable by mainstream countermeasures. We propose an imperceptible sample-specific backdoor that the trigger varies from sample to sample and invisible. Our trigger generation is automated through a desnoising autoencoder that is fed with delicate but pervasive features (i.e., edge patterns per images). We extensively experiment our backdoor attack on ImageNet and MS-Celeb-1M, which demonstrates stable and nearly 100% (i.e., 99.8%) attack success rate with negligible impact on the clean data accuracy of the infected model. The denoising autoeconder based trigger generator is reusable or transferable across tasks (e.g., from ImageNet to MS-Celeb-1M), whilst the trigger has high exclusiveness (i.e., a trigger generated for one sample is not applicable to another sample). Besides, our proposed backdoored model has achieved high evasiveness against mainstream backdoor defenses such as Neural Cleanse, STRIP, SentiNet and Fine-Pruning. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()