Core Concepts
Deepfake detection framework focuses on extracting broader forgery clues for improved accuracy and generalizability.
Abstract
Deepfake technology has led to widespread confusion and deception.
Current deepfake detection approaches may overfit and lack theoretical constraints.
Proposed framework extracts multiple non-overlapping local representations for global semantic-rich features.
Information bottleneck theory used to ensure task-relevant information extraction.
Empirical results show state-of-the-art performance on benchmark datasets.
Stats
Deepfake technology has given rise to a spectrum of novel and compelling applications.
Current deepfake detection approaches may easily fall into the trap of overfitting.
Proposed method achieves state-of-the-art performance on five benchmark datasets.
Quotes
"Deepfake technology has given rise to a spectrum of novel and compelling applications."
"Current deepfake detection approaches may easily fall into the trap of overfitting."
"Our method achieves state-of-the-art performance on five benchmark datasets."