7 papers
Exploring Active Data Selection Strategies for Continuous Training in Deepfake Detection
Yoshihiko Furuhashi, Junichi Yamagishi, Xin Wang +2
In deepfake detection, it is essential to maintain high performance by adjusting the parameters of the detector as new deepfake methods emerge. In this paper, we propose a method t…
Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera Systems
Muyao Niu, Zhuoxiao Li, Yifan Zhan +3
Many surveillance cameras switch between daytime and nighttime modes based on illuminance levels. During the day, the camera records ordinary RGB images through an enabled IR-cut f…
Mitigating Backdoor Attacks using Activation-Guided Model Editing
Felix Hsieh, Huy H. Nguyen, AprilPyone MaungMaung +2
Backdoor attacks compromise the integrity and reliability of machine learning models by embedding a hidden trigger during the training process, which can later be activated to caus…
Variational Autoencoder for Anomaly Detection: A Comparative Study
Huy Hoang Nguyen, Cuong Nhat Nguyen, Xuan Tung Dao +3
This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behav…
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis
Huy H. Nguyen, Junichi Yamagishi, Isao Echizen
This paper investigates the effectiveness of self-supervised pre-trained vision transformers (ViTs) compared to supervised pre-trained ViTs and conventional neural networks (ConvNe…
LookupForensics: A Large-Scale Multi-Task Dataset for Multi-Phase Image-Based Fact Verification
Shuhan Cui, Huy H. Nguyen, Trung-Nghia Le +2
Amid the proliferation of forged images, notably the tsunami of deepfake content, extensive research has been conducted on using artificial intelligence (AI) to identify forged con…