Deepfake Detection in Image Sequences: A Temporal Approach for Anomaly Detection

The rapid development of deepfake technology has led to the generation of a large amount of tampered video and image content, posing a major challenge to content authenticity verification. In particular, detecting deepfakes in image sequences (e.g., agricultural product packaging) is particularly difficult because the anomalies introduced by the tampering techniques are often subtle and temporally continuous. In this paper, we propose a new deepfake detection method based on time series, combining independent component analysis (FastICA) with anomaly detection techniques. We first apply FastICA to extract independent components from image sequences to identify anomalous visual patterns that are unique to deepfake tampering. In addition, we use an efficient anomaly detection algorithm, LSHiforest, to achieve scalable and accurate identification of suspicious sequences. Experimental results show that the proposed method can still detect deepfake content with high accuracy in challenging scenarios with complex temporal dynamics. Our work provides a promising solution for real-time and large-scale detection of deepfake content in dynamic media.