4 accepted papers
With the increasing size of Large Vision-Language Models (LVLMs), network pruning techniques aimed at compressing models for deployment in resource-constrained environments have garnered significant attention. However, we observe that pruning often leads to a degradation in safety performance. To ad…
The emergence of fine-tuning-as-a-service has revealed a new vulnerability in large language models (LLMs). A mere handful of malicious data uploaded by users can subtly manipulate the fine-tuning process, leading to a compromised alignment state. Existing methods to counteract fine-tuning attacks t…
Physical world adversarial attack is a highly practical and threatening attack, which fools real world deep learning systems by generating conspicuous and maliciously crafted real world artifacts. In physical world attacks, evaluating naturalness is highly emphasized since human can easily detect an…
Challenges caused by the acquisition condition of the images, the state of the objects, or the noise in the transmission of the images commonly exist in object detection. In those situations, the features of the objects extracted by CNNs contain certain uncertainty, which increases the difficulty of…