Attention-Based Adversarial Attacks

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12 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment
Aug 25, 2026Xitong Zeng, Zhaoge Bi, Yitian Yang +2Attention-Based Adversarial Attacks Adversarial Robustness
School of Electrical and Computer Engineering The University of Sydney Sydney, Australia · School of Computing Macquarie University Sydney, Australia
Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers
Aug 7, 2026Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando +6Adversarial Attacks Adversarial Robustness
Signal Processing, Artificial Intelligence and Vision Technologies Group (SAIVT), Queensland University of Technology · Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore · School of Computing, National University of Singapore +1
I2VShield: An Efficient Proactive Defense Framework against DiT-based Image-to-Video Models
Jul 28, 2026Yimao Guo, Zuomin Qu, Wei LuImage-to-Video Generation Adversarial Attacks
School of Computer Science and Engineering, Sun Yat-sen University Ministry of Education Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Guangzhou 510006, China · Guangdong Province Key Laboratory of Information Security Technology Guangzhou 510006, China · State Key Laboratory of HVDC, China Southern Power Grid Electric Power Research Institute
Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT
Jul 8, 2026Giulia Marchiori Pietrosanti, Giulio Rossolini, Giorgio ButtazzoAdversarial Patch Attacks Adversarial Attacks
Department of Excellence in Robotics and AI, Sant’Anna School of Advanced Study, Italy
Attention Hijacking: Response Manipulation Across Queries in Vision-Language Models
May 17, 2026Zhiqiang Wang, Dongrui Liu, Yan Li +4Adversarial Attacks on VLMs Adversarial Attacks
Hong Kong University of Science and Technology · Shanghai Jiao Tong University · Beihang University
Attention Is Where You Attack
Apr 30, 2026Aviral Srivastava, Sourav PandaSelf-Attention Adversarial Attacks
Amazon · The Pennsylvania State University
Seeing No Evil: Blinding Large Vision-Language Models to Safety Instructions via Adversarial Attention Hijacking
Apr 11, 2026Jingru Li, Wei Ren, Tianqing ZhuJailbreak Attacks Adversarial Attacks on VLMs
China University of Geosciences, Wuhan · City University of Macau
Complementary Text-Guided Attention for Zero-Shot Adversarial Robustness
Mar 19, 2026Lu Yu, Haiyang Zhang, Changsheng XuAttention-Based Adversarial Attacks Contrastive Learning for VLMs
School of Computer Science and Engineering, Tianjin University of Technology, 300384, China · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China · School of Artificial Intelligence, University of the Chinese Academy of Sciences, Beijing, 100049, China
Depth Charge: Jailbreak Large Language Models from Deep Safety Attention Heads
Mar 6, 2026Jinman Wu, Yi Xie, Shiqian Zhao +1Adversarial Attacks on LLMs LLM Jailbreak Attacks
Xidian University, Xi’an, China · Tsinghua University, Shenzhen, China · Nanyang Technological University, Singapore
Advantage-based Temporal Attack in Reinforcement Learning
Feb 23, 2026Shenghong HeAttention-Based Adversarial Attacks Adversarial Attacks
the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
PA-Attack: Guiding Gray-Box Attacks on LVLM Vision Encoders with Prototypes and Attention
Feb 23, 2026Hefei Mei, Zirui Wang, Chang Xu +2Attention-Based Adversarial Attacks Adversarial Attacks on VLMs
City University of Hong Kong · The University of Sydney
Stage-wise Attention-Guided Region Sequencing for Adversarial Attacks on Large Vision-Language Models
Feb 4, 2026Jaehyun Kwak, Nam Cao, Boryeong Cho +3Adversarial Attacks on VLMs Adversarial Attacks
KAIST · KENTECH

Aug 25, 2026J/K move · Enter open · S save

FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment

Xitong Zeng, Zhaoge Bi, Yitian Yang, Huaming Chen+1

School of Electrical and Computer Engineering The University of Sydney Sydney, Australia · School of Computing Macquarie University Sydney, Australia

Attention-Based Adversarial Attacks Adversarial Robustness Adversarial Attacks Financial Fraud Detection Credit Scoring

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Machine learning models are widely used in financial fraud and credit-risk detection, yet their adversarial robustness remains difficult to evaluate because financial tabular data involve domain-specific constraints, severe class imbalance, and asymmetric attacker capability. We argue that, in this setting, robustness is not only an attribute of the model, but also an attribute of the evaluation protocol. Different ways of enforcing constraints and capability can lead to substantially different robustness conclusions. This paper presents FraudBench, a protocol-sensitive benchmark for adversarial robustness evaluation in financial fraud and credit-risk detection. Rather than treating domain constraints as post-hoc validity checks, FraudBench evaluates the same dataset--model--attack--defence setting under three matched protocols: unconstrained attacks, post-hoc feasibility filtering, and deployment-aware constraint-integrated attacks. FraudBench covers four public financial datasets, and evaluates neural, tree-based, and ensemble models using three attack settings. Our results show that robustness conclusions are highly protocol-sensitive. On Lending Club Loan Data under the white-box setting, post-hoc filtering leaves only 3.7 feasible-flipped examples on average, whereas in-attack projection with attacker mutability masking produces 2,832.3 feasible-flipped examples under the same perturbation budget. The results on IEEE-CIS further show that feasibility and attacker capability are separate axes, while black-box evaluation shows that protocol choice can alter model-family rankings. These findings suggest that fraud robustness evaluation should report predictive degradation and attack feasibility jointly, and should incorporate domain constraints into attack generation rather than treating them as post-processing checks.