collaborators

15 papers

cs.SD2026

From Inaudible Inputs to Model Failures: Low-Frequency Safety Risks in LALMs

Yuanhe Zhang, Weiliu Wang, Jie Ren +7

Large audio-language models (LALMs) have demonstrated strong capabilities in understanding diverse audio inputs. This diversity includes low-frequency signals that are inaudible to…

cs.CR2026

Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle

Jiaming Zhang, Boyang Chen, Zherui Li +14

Once visual content enters an AI pipeline, its owner often retains little technical control over how it is used. Legal and regulatory remedies can address misuse, but many technica…

cs.AI2026

Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models

Junyao Yang, Chen Qian, Kun Wang +4

The advancement of Large Reasoning Models (LRMs) has catalyzed a paradigm shift from reactive ``fast thinking'' text generation to systematic, step-by-step ``slow thinking'' reason…

cs.SD2026

ChronosAudio: A Comprehensive Long-Audio Benchmark for Evaluating Audio-Large Language Models

Kaiwen Luo, Liang Lin, Yibo Zhang +8

Although Audio Large Language Models (ALLMs) have witnessed substantial advancements, their long audio understanding capabilities remain unexplored. A plethora of benchmarks have b…

cs.CR2026

Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs

Leitao Yuan, Qinghua Mao, Daizong Liu +5

Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate encoders can generalize to clo…

cs.SD2026

A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook

Kaiwen Luo, Zhenhong Zhou, Leo Wang +34

Advances in Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs). Among these, Large Audio Language Models (LALMs) are essential for realizi…