Performance Decay in Deepfake Detection: The Limitations of Training on Outdated Data

The continually advancing quality of deepfake technology exacerbates the threats of disinformation, fraud, and harassment by making maliciously-generated synthetic content increasingly difficult to distinguish from reality. We introduce a simple yet effective two-stage detection method that achieves an AUROC of over 99.8% on contemporary deepfakes. However, this high performance is short-lived. We show that models trained on this data suffer a recall drop of over 30% when evaluated on deepfakes created with generation techniques from just six months later, demonstrating significant decay as threats evolve. Our analysis reveals two key insights for robust detection. Firstly, continued performance requires the ongoing curation of large, diverse datasets. Second, predictive power comes primarily from static, frame-level artifacts, not temporal inconsistencies. The future of effective deepfake detection therefore depends on rapid data collection and the development of advanced frame-level feature detectors.

2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures

The rapid advancement of machine learning technologies raises questions about the security of machine learning models, with respect to both training-time (poisoning) and test-time (evasion, impersonation, and inversion) attacks. Models performing image-related tasks, e.g. detection, and classification, are vulnerable to adversarial attacks that can degrade their performance and produce undesirable outcomes. This paper introduces a novel technique for anomaly detection in images called 2DSig-Detect, which uses a 2D-signature-embedded semi-supervised framework rooted in rough path theory. We demonstrate our method in adversarial settings for training-time and test-time attacks, and benchmark our framework against other state of the art methods. Using 2DSig-Detect for anomaly detection, we show both superior performance and a reduction in the computation time to detect the presence of adversarial perturbations in images.