WebMay 11, 2024 · In contrast, backdoor attack aims to implant triggers into a model during the training stage, such that the victim model acts normally on the clean data unless a trigger is present in a sample. This work follows a typical setting of clean-label backdoor attack, where a few poisoned samples (with their content tampered yet labels unchanged) are ... WebFeb 1, 2024 · We also propose three target label selection strategies to achieve different goals. Experimental results indicate that our clean-image backdoor can achieve a 98% attack success rate while preserving the model's functionality on the benign inputs. Besides, the proposed clean-image backdoor can evade existing state-of-the-art defenses.

Kallima : A Clean-Label Framework for Textual Backdoor Attacks

WebJun 10, 2024 · Towards stealthiness, researchers propose clean-label backdoor attacks, which require the adversaries not to alter the labels of the poisoned training datasets. Clean-label settings make the attack more stealthy due to the correct image-label pairs, but some problems still exist: first, traditional methods for poisoning training data are ... WebMar 6, 2024 · Clean-Label Backdoor Attacks on Video Recognition Models. Deep neural networks (DNNs) are vulnerable to backdoor attacks which can hide backdoor … tsnow-on-the-web/tsnowhome.html

Clean-label poisoning attack with perturbation causing dominant ...

WebSep 25, 2024 · In this paper, we propose Kallima, the first clean-label framework for synthesizing poisoned samples to develop insidious textual backdoor attacks (see Fig. 2 … WebSep 25, 2024 · 4.1 Key Intuition. To address the challenges in Sect. 3.2, we propose the first clean-label framework Kallima to synthesize hard-to-learn samples from the target class, hence causing the model to enhance the effectiveness of the backdoor trigger. The key intuition of our framework is shown in Fig. 1.There are two classes A and B, where B is … WebMar 15, 2024 · The classification accuracy of clean samples can keep unchanged, and the success rate of backdoor attack is equivalent to random guess, and the backdoor samples will be predicted as correct labels by classifiers, regardless of the problem of classifiers are injected into the backdoor. tsnp2std microsoft