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Works108 from public data
- MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models328
Analysis across 12 state-of-the-art models reveals that MLLMs are susceptible to breaches instigated by the approach, even when the equipped LLMs have been safety-aligned, and proposes a straightforward yet effective prompting strategy to enhance the resilience of MLLMs against these types of attacks.
- A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models254
This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models, image-text matching models, and text- to-image generation models.
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- A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment139
This paper introduces the concept of "full-stack" safety to systematically consider safety issues throughout the entire process of LLM training, deployment, and eventual commercialization, and represents the first safety survey to encompass the entire lifecycle of LLMs.
- Improved Techniques for Optimization-Based Jailbreaking on Large Language Models134
Improved techniques for optimization-based jailbreaks like GCG are presented and demonstrated that they can help GCG outperform state-of-the-art jailbreaking attacks and achieve nearly 100% attack success rate.
- Understanding Individual Decisions of CNNs via Contrastive Backpropagation108
Contrastive Layer-wise Relevance Propagation is proposed, which is capable of producing instance-specific, class-discriminative, pixel-wise explanations and both qualitative and quantitative evaluations show that the CLRP generates better explanations than the LRP.
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- Backdoor Defense via Adaptively Splitting Poisoned Dataset98
This work argues that the core of training-time defense is to select poisoned samples and to handle them properly, and proposes an adaptively splitting dataset-based defense (ASD), which successfully defends against backdoor attacks during training.
- Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images94
It is found that high energy-latency cost during inference of VLMs can be manipulated by maximizing the length of generated sequences by proposing verbose images, with the goal of crafting an imperceptible perturbation to induce VLMs to generate long sentences during inference.
- Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation90
This work proposes a novel self-supervised approach to find interpretable latent directions for a given concept in an interpretable latent space of diffusion models as seman-tic concepts, and discovers vectors related to inappropriate concepts.
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- Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety76
This survey provides a systematic review of current safety research on large models, covering Vision Foundation Models, Large Language Models, Vision-Language Pre-training models, Vision-Language Models, Diffusion Models, and large-model-powered Agents, and presents a comprehensive taxonomy of safety threats to these models.
- A Survey on Transferability of Adversarial Examples across Deep Neural Networks76
This survey explores the landscape of the adversarial transferability of adversarial examples and categorizes existing methodologies to enhance adversarial transferability, and discusses the fundamental principles guiding each approach.
- Improving the Robustness of Capsule Networks to Image Affine Transformations67
It is revealed that the routing procedure contributes neither to the generalization ability nor to the affine robustness of the CapsNets, and an affine CapsNet (Aff-CapsNets) is proposed, which are more robust to affine transformations.
- An Image Is Worth 1000 Lies: Adversarial Transferability across Prompts on Vision-Language Models61
This work proposes the Cross-Prompt Attack (CroPA), a method that updates the visual adversarial perturbation with learnable prompts, which are designed to counteract the misleading effects of the adversarial image.
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- Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds54
A novel shape-based adversarial attack method, HiT-ADV, which initially conducts a two-stage search for attack regions based on saliency and imperceptibility scores, and then adds deformation perturbations in each attack region using Gaussian kernel functions, which is extendable to physical attack.
- FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation53
The proposed Federated Representation Augmentation (FRAug) substantially outperforms the current state-of-the-art FL methods for feature distribution shifts, including PartialFed and FedBN.
- When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models49
This survey provides a comprehensive overview of the methodologies enabling LLMs to process, understand, and generate 3D data, and examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue.
- Interpretable Graph Capsule Networks for Object Recognition45
This work proposes interpretable Graph Capsule Networks (GraCapsNets), where the routing part is replaced with a multi-head attention-based Graph Pooling approach, and individual classification explanations can be created effectively and efficiently.
- OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization44
The proposed method formulates the features of image and text sets as two distinct distributions and employs optimal transport theory to determine the most efficient mapping between them, which informs the generation of adversarial examples to effectively counteract the overfitting issues.
- Capsule Network is Not More Robust than Convolutional Network42
This paper carefully examines the special designs in CapsNet that differ from that of a ConvNet commonly used for image classification, and proposes enhanced ConvNets simply by introducing the essential components behind the CapsNet’s success.
- Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?37
GPT4 and GPT-4V demonstrate better robustness against jailbreak attacks compared to open-source LLMs and MLLMs, and Llama2 and Qwen-VL-Chat are more robust compared to other open-source models.
- Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?33
A simple yet effective approach is proposed, termed Mixed Modality In-Context Example Selection (MMICES), which considers both visual and language modalities when selecting demos, which revealed that multimodal ICL is predominantly driven by the textual content whereas the visual information in the demos has little influence.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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