Switchable backdoor attack in vision transformers via progressive dual-token injection from shallow to deep layers

Secure Hardware Accelerator for Resilience Against Adversarial Attacks in ViTs

Vision Transformers (ViTs) have demonstrated exceptional performance across various computer vision tasks. However, their susceptibility to adversarial white-box attacks poses a significant risk, especially in safety-critical applications. These attacks, with full model access, pose severe threat by drastically reducin...

Robust backdoor attacks against fine-tuning-based transfer learning via self-attention distillation

MROP: Mask-Region Optimized Purification Against Backdoor Attack in Deep JSCC

Deep joint source and channel coding (JSCC) transmits a source by mapping it directly to channel symbols through an end-to-end deep neural network (DNN) and reconstructing it at the receiver. Taking image transmission as an application, this DNN pipeline behaves as a black box: the receiver cannot readily detect securi...

Backdoor Attacks on CLIP via Prompt Learning.

This work introduces BadCLIP, a novel backdoor attack that leverages prompt learning to effectively inject malicious behaviors into CLIP models even in data-scarce scenarios and achieves high attack success rates while maintaining comparable accuracy on clean images.

Detection: Combining Statistical, Transformer, Zero-Shot, and Watermark Methods with Adversarial Robustness and Explainability

Beyond Small Patches: Black-Box Detection and Purification of Diverse Backdoor Triggers

TRIM (Trigger Removal by Identifying Manipulated Regions), a deployment-oriented black-box defense that detects and selectively removes backdoor triggers at inference time without requiring model internals, training data, or clean samples, is proposed.