Adversarial Purification

Latest papers

13 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Detection-Guided Adaptive Purification with Diffusion Models for Robust Audio Deepfake Detection
Oct 7, 2026Muhammed Salih Kayhan, Qiben YanAudio Deepfake Detection Robustness Audio Deepfake Detection
Michigan State University, East Lansing, MI, USA
Adversarial Deepfake Generation and an Investigation of Purification-Based Adversarial Detection
Jul 28, 2026Junghyun Kim, Seunghyun Kim, Jiyoung WooDeepfake Detection AI-Generated Image Detection
Department of AI and Big Data Engineering, Soonchunhyang University, Asan, South Korea
MAPE: Defending Against Transferable Adversarial Attacks Using Multi-Source Adversarial Perturbations Elimination
Jun 30, 2026Xinlei Liu, Jichao Xie, Tao Hu +4Adversarial Robustness Adversarial Perturbations
Information Engineering University, Zhengzhou, 450002, China · Key Laboratory of Cyberspace Security, Ministry of Education, Zhengzhou, 450002, China
Stain-Aware Wavelet Regularization for Instant Adversarial Purification in Histopathology
Jun 7, 2026Zhe Li, Bernhard KainzComputational Pathology Adversarial Robustness
FAU Erlangen-Nürnberg Erlangen, Germany · Imperial College London London, UK
Structure-Guided Visual Perturbation Neutralization for LVLMs
May 27, 2026Yuanhe Zhang, Xueting Wang, YanBin Ren +6VLM Robustness Adversarial Attacks on VLMs
1Beijing University of Posts and Telecommunications · University of Science and Technology of China · 3JIUTIAN Research +2
Self-supervised Adversarial Purification for Graph Neural Networks
May 22, 2026Woohyun Lee, Hogun ParkGraph Neural Networks Adversarial Robustness
Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea.
TARO: Temporal Adversarial Rectification Optimization Using Diffusion Models as Purifiers
May 8, 2026Daniel Wesego, Pedram RooshenasDiffusion Model Guidance Adversarial Robustness
Department of Computer Science · University of Illinois Chicago · Chicago, IL 60607
Memory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations
May 7, 2026Yuan Du, Mitchel Hill, HanQin CaiAdversarial Robustness Adversarial Purification
Department of Statistics and Data Science University of Central Florida Orlando, FL 32816, USA · InnoPeak Technology Palo Alto, CA 94303, USA · Department of Statistics and Data Science Department of Computer Science University of Central Florida Orlando, FL 32816, USA
Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
Apr 30, 2026Emma Andrews, Sahan Sanjaya, Prabhat MishraAdversarial Robustness Quantum Machine Learning
University of Florida, Gainesville, Florida, USA
APC: Transferable and Efficient Adversarial Point Counterattack for Robust 3D Point Cloud Recognition
Apr 17, 2026Geunyoung Jung, Soohong Kim, Inseok Kong +1Point Cloud Classification Adversarial Robustness
Department of Artificial Intelligence, University of Seoul, South Korea · Department of Geo Informatics, University of Seoul, South Korea
A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large Vision-Language Models
Mar 27, 2026Mujtaba Hussain Mirza, Antonio D'Orazio, Odelia Melamed +1Adversarial Purification Adversarial Robustness
OmnAI Lab, Computer Science Department, Sapienza University of Rome, Italy · Weizmann Institute of Science, Israel
Diffusion-Based Feature Denoising and Using NNMF for Robust Brain Tumor Classification
Mar 13, 2026Hiba Adil Al-kharsan, Róbert RajkóAdversarial Purification Medical Image Classification
Doctoral School of Computer Science, University of Szeged, Árpád tér 2, H-6720 Szeged, Hungary · University Research and Innovation Center (EKIK), Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary · Academic Staff, Doctoral School of Computer Science, University of Szeged, Árpád tér 2, H-6720 Szeged, Hungary
Purify Once, Edit Freely: Breaking Image Protections under Model Mismatch
Mar 13, 2026Qichen Zhao, Shengfang Zhai, Xinjian Bai +4Adversarial Purification Adversarial Robustness
Peking University · National University of Singapore · The University of Western Australia

Oct 7, 2026J/K move · Enter open · S save

Detection-Guided Adaptive Purification with Diffusion Models for Robust Audio Deepfake Detection

Muhammed Salih Kayhan, Qiben Yan

Michigan State University, East Lansing, MI, USA

Audio Deepfake Detection Robustness Audio Deepfake Detection Adversarial Robustness Adversarial Purification Adversarial Example Detection Adversarial Attacks

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Audio deepfake detectors remain vulnerable to adversarial perturbations that suppress the acoustic cues used for detection, allowing manipulated utterances to evade the detector. Although existing defenses can improve robustness, they require retraining the detector or introduce additional distortion. Diffusion-based purification instead leaves the pretrained detector unchanged, but existing methods use the same purification strength for all inputs, creating a trade-off between removing adversarial perturbations and preserving the subtle spoofing cues needed for detection. In this paper, we propose Detection-Guided Adaptive Purification (DGAP), a diffusion-based defense that adjusts purification strength per input. Building on the observation that a light purification perturbs the detector score of an adversarial input far more than that of a benign one, the framework uses the resulting score shift as a reference-free indicator of adversarial manipulation. Inputs with small shifts are passed unchanged, whereas flagged inputs undergo stronger purification before final detection. We evaluate the framework against three adversarial attack settings across three deepfake detectors, and compare it with nine existing defenses. Our results show that DGAP achieves the best defense performance across all detectors while leaving benign inputs nearly unaffected, and remains effective under the defense-aware adaptive attack.