← Back to all news
arXiv Computation and Language By Yifan Lan, Zhongye Liu, Yurui Chang, Hanyu Wang, Yongkang Du, Lu Lin, Jinghui Chen

DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision

Read the original on arXiv Computation and Language →

DecisionRobust is a new benchmark that evaluates the robustness of JEV-style decision models to adversarial attacks across both text and vision inputs. It includes a variety of attack types—semantic text perturbations, image perturbations, and prompt injections—and uses external validity checks and cross-model candidate selection to curate evaluation sets. Experiments on 30 text and 17 vision model configurations reveal that vulnerabilities can transfer across models, robustness varies by task and attack, and label-consistency checks significantly reduce apparent attack success.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv AI
Aug 20

Breaking the weakest link to evade vision language models

The paper investigates how Vision Language Models (VLMs) can be fooled by small, human‑imperceptible changes to images. It introduces a gradient‑based attack that targets only the vision encoder, reducing computational cost while still effectively disrupting both untargeted and targeted multimodal alignment. Experiments on open‑source VLMs such as Qwen2.5‑VL, Granite‑Vision, FastVLM, and Phi‑3.5‑Vision demonstrate that these perturbations can significantly alter the models’ textual outputs.

By Ilan Zini, Boussad Addad, Katarzyna Kapusta
llmsroboticsmultimodalsafety
More like this →
Hugging Face Trending Papers
Aug 19

Breaking the weakest link to evade vision language models

The paper investigates how Vision Language Models (VLMs) can be fooled by tiny, human‑imperceptible changes to images. It introduces a gradient‑based attack that targets only the vision encoder, reducing computational cost while still effectively disrupting both untargeted and targeted multimodal interpretations. Experiments on open‑source VLMs such as Qwen2.5‑VL, Granite‑Vision, FastVLM, and Phi‑3.5‑Vision demonstrate that these small perturbations can dramatically alter the models’ textual outputs.

llmsroboticsmultimodalsafety
More like this →
arXiv AI
Aug 19

DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization

DiSCO is a zero‑shot, black‑box defense for text‑to‑image models that operates solely at the prompt level. It expands prompts with a distribution‑guided suffix using beam search and contrastive scoring against safe and unsafe image pools generated by the target model, iteratively refining until safe content is produced. The method improves safety on the I2P benchmark under various red‑teaming attacks, reducing attack success rates by 37.7% and 25.13% while preserving semantic fidelity and image coherence.

By Tong Zhang, Motasem Alfarra, Carlos Hinojosa, Christos Louizos, Bernard Ghanem
llmsdiffusionfine-tuningmultimodalbenchmarkssafety
More like this →