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- Perceptual-Sensitive GAN for Generating Adversarial Patches369
This paper proposes a perceptual-sensitive generative adversarial network (PS-GAN) that can simultaneously enhance the visual fidelity and the attacking ability for the adversarial patch, and treats the patch generation as a patch-to-patch translation via an adversarial process.
- Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World282
The Dual Attention Suppression (DAS) attack is proposed to generate visually-natural physical adversarial camouflages with strong transferability by suppressing both model and human attention and outperforms state-of-the-art methods.
- Bias-Based Universal Adversarial Patch Attack for Automatic Check-Out170
A bias-based framework to generate class-agnostic universal adversarial patches with strong generalization ability, which exploits both the perceptual and semantic bias of models.
- BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning145
The BadCLIP attack is introduced, which is resistant to backdoor detection and model fine-tuning defenses and draws motivations from the perspective of the Bayesian rule and proposes a dual-embedding guided framework for backdoor attacks.
- Training Robust Deep Neural Networks via Adversarial Noise Propagation145
A simple yet powerful training algorithm, named Adversarial Noise Propagation (ANP), which injects noise into the hidden layers in a layer-wise manner and enables the strong robustness for deep models against both adversarial and corrupted ones, and also significantly outperforms various adversarial defense methods.
- Jailbreak Vision Language Models via Bi-Modal Adversarial Prompt140
The Bi-Modal Adversarial Prompt Attack (BAP) is introduced, which executes jailbreaks by optimizing textual and visual prompts cohesively and significantly outperforms other methods by large margins.
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- BiBERT: Accurate Fully Binarized BERT124
BiBERT, an accurate fully binarized BERT, is proposed to eliminate the performance bottlenecks and introduces an efficient Bi-Attention structure for maximizing representation information statistically and a Direction-Matching Distillation scheme to optimize the full binarization BERT accurately.
- A comprehensive evaluation framework for deep model robustness105
This paper establishes a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i.e., data and model) and provides a comprehensive evaluation framework, where researchers could conduct comprehensive and fast evaluations using the open-source toolkit.
- X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection102
This paper proposes X-adv to generate physically printable metals that act as an adversarial agent capable of deceiving X-ray detectors when placed in luggage, and develops a differentiable converter that facilitates the generation of 3D-printable objects with adversarial shapes.
- Spatiotemporal Attacks for Embodied Agents99
This work generates spatiotemporal perturbations to form 3D adversarial examples, which exploit the interaction history in both the temporal and spatial dimensions and adversarially perturb the physical properties of the contextual objects that appeared in the most important scene views.
- SafeBench: A Safety Evaluation Framework for Multimodal Large Language Models84
SafeBench is a comprehensive framework designed for conducting safety evaluations of MLLMs that draws inspiration from the jury system in judicial proceedings and pioneer the jury deliberation evaluation protocol that adopts collaborative LLMs to evaluate whether target models exhibit specific harmful behaviors, providing a reliable and unbiased assessment of content security risks.
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- Transductive Relation-Propagation Network for Few-shot Learning76
This work proposes a transductive relation-propagation graph neural network (TRPN) to explicitly model and propagate such relations across support-query pairs, the first work that explicitly takes the relations of support- query pairs into consideration in few-shot learning.
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- Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs70
A Semantic Mirror Jailbreak (SMJ) approach that bypasses LLMs by generating jailbreak prompts that are semantically similar to the original question is introduced and is resistant to defenses that use those metrics as thresholds.
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- Harnessing Perceptual Adversarial Patches for Crowd Counting66
The Perceptual Adversarial Patch generation framework is proposed to tailor the adversarial perturbations for crowd counting scenes using the model-shared perceptual features and an adaptive crowd density weighting approach is handcrafted to capture the invariant scale perception features across various models.
- Coarse-to-Fine Image Inpainting via Region-wise Convolutions and Non-Local Correlation65
This work proposes region-wise convolutions to locally deal with the different types of regions to help exactly reconstruct existing regions and roughly infer the missing ones from existing regions at the same time, and introduces a non-local operation to globally model the correlation among different regions.
- Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift61
A multimodal attribution backdoor attack (MABA) is proposed that injects domain-agnostic triggers into critical areas using attributional interpretation and boosts the attack success rate of generalization over the unimodal attack.
- Manipulating Multimodal Agents via Cross-Modal Prompt Injection57
This paper proposes CrossInject, a novel attack framework in which attacker embeds adversarial perturbations across multiple modalities to align with target malicious content, allowing external instructions to hijack the agents' decision-making process and execute unauthorized tasks.
- Regionwise Generative Adversarial Image Inpainting for Large Missing Areas57
A generic inpainting framework capable of handling incomplete images with both contiguous and discontiguous large missing areas is proposed, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, on the large contiguousand discontiguous missing areas.
- Pre-trained Trojan Attacks for Visual Recognition52
The Pre-trained Trojan attack is proposed, which embeds backdoors into a PVM, enabling attacks across various downstream vision tasks, and addresses the issue of shortcut connections by introducing a context-free learning pipeline for poison training.
- Unveiling the Safety of GPT-4o: An Empirical Study using Jailbreak Attacks52
This paper adopts a series of multi-modal and uni-modal jailbreak attacks on 4 commonly used benchmarks encompassing three modalities encompassing three modalities, and conducts a rigorous evaluation of GPT-4o against jailbreak attacks.
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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 2026-10-12. 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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