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- LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model(arXiv)
Author : Yuxin Cao, Jinghao Li, Xi Xiao, Derui Wang, Minhui Xue, Hao Ge, Wei Liu, Guangwu Hu
Abstract : Previous work has shown that well-crafted adversarial perturbations can threaten the security of video recognition systems. Attackers can invade such models with a low query budget when the perturbations are semantic-invariant, such as StyleFool. Despite the query efficiency, the naturalness of the minutia areas still requires amelioration, since StyleFool leverages style transfer to all pixels in each frame. To close the gap, we propose LocalStyleFool, an improved black-box video adversarial attack that superimposes regional style-transfer-based perturbations on videos. Benefiting from the popularity and scalably usability of Segment Anything Model (SAM), we first extract different regions according to semantic information and then track them through the video stream to maintain the temporal consistency. Then, we add style-transfer-based perturbations to several regions selected based on the associative criterion of transfer-based gradient information and regional area. Perturbation fine adjustment is followed to make stylized videos adversarial. We demonstrate that LocalStyleFool can improve both intra-frame and inter-frame naturalness through a human-assessed survey, while maintaining competitive fooling rate and query efficiency. Successful experiments on the high-resolution dataset also showcase that scrupulous segmentation of SAM helps to improve the scalability of adversarial attacks under high-resolution data.
2. Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation(arXiv)
Author : Mingzhou Jiang, Jiaying Zhou, Junde Wu, Tianyang Wang, Yueming Jin, Min Xu
Abstract : The Segment Anything Model (SAM) gained significant success in natural image segmentation, and many methods have tried to fine-tune it to medical image segmentation. An efficient way to do so is by using Adapters, specialized modules that learn just a few parameters to tailor SAM specifically for medical images. However, unlike natural images, many tissues and lesions in medical images have blurry boundaries and may be ambiguous. Previous efforts to adapt SAM ignore this challenge and can only predict distinct segmentation. It may mislead clinicians or cause misdiagnosis, especially when encountering rare variants or situations with low model confidence. In this work, we propose a novel module called the Uncertainty-aware Adapter, which efficiently fine-tuning SAM for uncertainty-aware medical image segmentation. Utilizing a conditional variational autoencoder, we encoded stochastic samples to effectively represent the inherent uncertainty in medical imaging. We designed a new module on a standard adapter that utilizes a condition-based strategy to interact with samples to help SAM integrate uncertainty. We evaluated our method on two multi-annotated datasets with different modalities: LIDC-IDRI (lung abnormalities segmentation) and REFUGE2 (optic-cup segmentation). The experimental results show that the proposed model outperforms all the previous methods and achieves the new state-of-the-art (SOTA) on both benchmarks. We also demonstrated that our method can generate diverse segmentation hypotheses that are more realistic as well as heterogeneous