The paper introduces TRIM, a black‑box defense for backdoor attacks in computer vision models. TRIM identifies and removes malicious trigger regions at inference time using region‑based segmentation, adaptive trigger discovery via inpainting and diffusion, and selective purification, without needing model internals, training data, or clean samples. Experiments on various datasets and trigger types show TRIM reduces attack success rates to as low as 1.16% while maintaining high clean accuracy.
By Ahmed Abdelnaby, Mohamed Elmahallawy
arXiv:2507. 05113v3 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) are susceptible to backdoor attacks, where adversaries poison training data to implant backdoor into the victim model.
By Binyan Xu, Fan Yang, Xilin Dai, Di Tang, Kehuan Zhang
arXiv:2604. 05809v2 Announce Type: replace-cross Abstract: This paper presents Text-Guided Backdoor (TGB), an adjustable backdoor attack against multimodal pretrained models that uses natural-word triggers, namely words that can naturally occur in ordinary textual inputs.
By Yiyang Zhang, Chaojian Yu, Ziming Hong, Yuanjie Shao, Qinmu Peng, Tongliang Liu, Xinge You
arXiv:2609.31558v1 Announce Type: new
Abstract: Contrastive Language--Image Pretraining (CLIP) has emerged as a dominant vision backbone due to its strong transferability and zero-shot capabilities....
By Ahmed Abdelnaby, Mohamed Elmahallawy
arXiv:2606. 02947v1 Announce Type: new Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks.
By Ivan Saboli\'c, Marin Or\v{s}i\'c, Josip \v{S}ari\'c, Sven Lon\v{c}ari\'c
The paper introduces Checkerboard, a clean‑label backdoor attack that uses a closed‑form, data‑independent trigger design based on an input‑space Fisher‑separability objective and a ridge four‑neighbor local‑smoothness prior. This approach yields a pixel‑wise checkerboard trigger without requiring data access, surrogate model training, or iterative optimization, and it outperforms existing norm‑bounded clean‑label attacks across four benchmark datasets. On CIFAR‑10, poisoning 20 samples with a 10/255 perturbation achieves a 95.72% attack success rate, while on IN‑100 a 0.4% global poisoning rate yields over 83% ASR without harming clean accuracy, and the attack remains robust against state‑of‑the‑art defenses.
By Yi Yang, Jinyang Huang, Binbin Liu, Feng-Qi Cui, Xiaokang Zhou, Haiming Jin, Zhi Liu, Jie Zhang, Meng Li
arXiv:2606. 26285v1 Announce Type: cross Abstract: Noise-based backdoor attacks on diffusion models typically rely on input-time trigger injection, untargeted activation, and out-of-distribution target generation.
By William Aiken, Paula Branco, Guy-Vincent Jourdan, Iosif-Viorel Onut
arXiv:2608. 00732v1 Announce Type: new Abstract: Backdoor attacks pose a serious threat to deep neural networks, especially when training relies on third-party data, allowing adversaries to inject malicious behaviors through data poisoning.
By Zixuan Zhu, Rui Wang, Lihua Jing, Jinwen Zhong
The paper introduces DistScan, a backdoor detection framework for object detection models that identifies malicious behavior by detecting shifts in the pre‑NMS prediction class distribution relative to training class frequencies. DistScan operates on clean validation data, requiring no access to model weights, trigger knowledge, or additional training, and it aggregates intermediate predictions to flag backdoored models. Experiments on MS‑COCO and PASCAL VOC across two architectures and three scene‑level attack scenarios show that DistScan outperforms existing methods, improving average detection accuracy by 27.32 percentage points over the best baseline.
By Longtian Wang, Zhengyu Zhao, Chenhao Lin, Le Yang, Shiwei Wang, Yuhan Zhi, Xiaofei Xie, Chao Shen
arXiv:2607. 05516v2 Announce Type: replace-cross Abstract: Model-specific adversarial attacks have been extensively studied.
By Paul K. Mandal, Pavan Reddy, Tristan Malatynski
arXiv:2607. 05516v1 Announce Type: cross Abstract: Model-specific adversarial attacks have been extensively studied.
By Paul K. Mandal, Pavan Reddy, Tristan Malatynski
arXiv:2511.14301v4 Announce Type: replace-cross
Abstract: Transformer-based models are highly susceptible to backdoor attacks via supervised fine-tuning (SFT). To red-team existing data-poisoning def...
By Eric Xue, Ruiyi Zhang, Pengtao Xie