Flexible Multi-Modality Deepfake Identification with Dynamic Learning and Cross-Modality Inconsistency

Rapid advances in generative models, that produce hyper-realistic modifications of photos and videos, have contributed to deepfake detection more difficult. Current detection approaches are very costly, making real-time detection difficult, and they often suffer with generalization, particularly when faced with unknown deepfake techniques. An Adaptive Multi-Modality Framework is proposed, presenting a unique set of methods for reliable and effective deepfake detection in order to overcome these drawbacks. The framework incorporates Self-Supervised Patch-Based Learning for smooth detection of localized manipulations, Dynamic Discriminator Learning (DDL) in adaptively evolve against new deepfake generation techniques, and Cross-Modality Inconsistency Scoring (CMIS) to detect anomalies across spatial, temporal, and audio-visual domains. The Inconsistency Score (IS), a novel metric that measures the probability of modified material, unifies these elements. This method allows for real-time application, improves detection accuracy, and guarantees flexibility in response to new threats. The investigations reveal that the proposed method has the potential to transform fake identification techniques by surpassing state-of-the-art techniques.