As deep neural networks (DNNs) become more prevalent, concerns over their security against backdoor attacks that implant hidden malicious functionalities have grown.  Cybersecurity researchers (Wenmin Chen and Xiaowei Xu) recently proposed DEBA, an invisible backdoor attack leveraging singular value decomposition (SVD) to embed imperceptible triggers during model training, causing predefined malicious behaviors. DEBA replaces minor visual features of trigger images with those from clean images, preserving major features for indistinguishability.  Invisible Backdoor Attack – DEBA Extensive evaluations show that DEBA achieves high attack success rates while maintaining the perceptual quality of poisoned images.

Source: GBHackers