15 accepted papers
Adversarial patch attacks pose a significant threat to visual systems. While current patch purification-based defense methods enhance core metrics of visual perception models, they overlook the critical issue of false positive patches, severely compromising image usability. This paper reveals the in
Unauthorized personalization based on diffusion models pose a severe and growing threat to digital privacy by enabling the unauthorized replication and exploitation of individual identities. Existing disrupting-based defenses primarily add invisible perturbations arbitrarily across the entire image
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. In this work, we reveal that backdoor behaviors tend to be absorbed by a simpler parallel branch when jointl
With the successful transition of Transformers from natural language processing (NLP) to computer vision (CV) domains, Vision Transformers (ViTs) have achieved state-of-the-art performance in many CV tasks. However, backdoor attacks, a significant threat in deep learning, also pose a risk to the sec…
High-quality open-source datasets are essential for advancing deep neural networks. However, the unauthorized commercial use of these datasets has raised significant concerns about copyright protection. One promising approach is backdoor watermark-based dataset ownership verification (BW-DOV), in wh…
The goal of the image change captioning task is to capture the differences between two similar images and describe them in natural language. In this paper, we decompose this task into two sub-problems, i.e., fine-grained change feature learning and discrimination of changed regions. Compared with ex
Knowledge Distillation (KD) has attracted considerable attention as a typical model compression and knowledge transfer paradigm. However, most KD approaches are predicated on the implicit assumption: the deployed student model will exclusively encounter classes that have been seen during training, w…
Large language models (LLMs) increasingly rely on lengthy prompts to achieve complex tasks. However, longer prompts not only increase inference costs but pose significant challenges for limited context windows of LLMs. Prompt compression aims to accelerate inference in long-text scenarios while pres…
Hallucinations in large vision-language models (LVLMs) pose significant challenges for real-world applications, as LVLMs may generate responses that appear plausible yet remain inconsistent with the associated visual content. This issue rarely occurs in human cognition. We argue that this discrepanc…
Autonomous systems leveraging visual perception face a rising threat from adversarial patches, jeopardizing their robustness. Existing defense methods adaptable to various pre-trained models typically rely on observed patch characteristics or prior attack data, having difficulty adapting to new thre
The remarkable progress of Latent Diffusion Models (LDMs) in image generation has raised concerns about the potential for unauthorized image mimicry. To address these concerns, studies on adversarial attacks against LDMs have gained increasing attention in recent years. However, existing methods fac
The goal of image change captioning is to capture the content differences between two images and describe them in natural language. The key is how to learn stable content changes from noise such as viewpoint and image structure. However, current work mostly focuses on identifying changes, and the in…
Open-Set Recognition (OSR) aims to accurately identify known classes while effectively rejecting unknown classes to guarantee reliability. Most existing OSR methods focus on learning in the spatial domain, where subtle texture and global structure are potentially intertwined. Empirical studies have…
Adversarial patch attacks present a significant threat to real-world object detectors due to their practical feasibility. Existing defense methods which rely on attack data or prior knowledge struggle to effectively address a wide range of adversarial patches. In this paper we show two inherent char…
Recently, backdoor attacks have posed a serious security threat to the training process of deep neural networks (DNNs). The attacked model behaves normally on benign samples but outputs a specific result when the trigger is present. However, compared with the rocketing progress of backdoor attacks,…