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Found 11 papers, 6 papers with code
CapGen:An Environment-Adaptive Generator of Adversarial Patches
Adversarial patches, often used to provide physical stealth protection for critical assets and assess perception algorithm robustness, usually neglect the need for visual harmony with the background environment, making them easily noticeable.
Global Challenge for Safe and Secure LLMs Track 1
This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks.
Prompt-Guided Environmentally Consistent Adversarial Patch
Adversarial attacks in the physical world pose a significant threat to the security of vision-based systems, such as facial recognition and autonomous driving.
Boosting Jailbreak Transferability for Large Language Models
Large language models have drawn significant attention to the challenge of safe alignment, especially regarding jailbreak attacks that circumvent security measures to produce harmful content.
Enhancing Transferability of Adversarial Examples with Spatial Momentum
For that, we propose a novel method named Spatial Momentum Iterative FGSM attack (SMI-FGSM), which introduces the mechanism of momentum accumulation from temporal domain to spatial domain by considering the context information from different regions within the image.
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
Generating Transferable Adversarial Patch by Simultaneously Optimizing its Position and Perturbations
In this paper, we propose a method to simultaneously optimize the position and perturbation to generate transferable adversarial patches, and thus obtain high attack success rates in the black-box setting.
An Effective and Robust Detector for Logo Detection
Moreover, we have applied the proposed methods to competition ACM MM2021 Robust Logo Detection that is organized by Alibaba on the Tianchi platform and won top 2 in 36489 teams.
Improving Adversarial Transferability with Gradient Refining
To improve the transferability of adversarial examples for the black-box setting, several methods have been proposed, e. g., input diversity, translation-invariant attack, and momentum-based attack.
Object Hider: Adversarial Patch Attack Against Object Detectors
Additionally, we have applied the proposed methods to competition "Adversarial Challenge on Object Detection" that is organized by Alibaba on the Tianchi platform and won top 7 in 1701 teams.
Sparse Black-box Video Attack with Reinforcement Learning
Adversarial attacks on video recognition models have been explored recently.
