An Improved Altfreezing based Video Face Forgery Detection

Abstract

Deepfake technology is a growing threat due to its strength to produce extremely realistic manipulated videos, often used maliciously to disseminate misinformation or explicit content. Destroying reputations and lives. Deepfakes are improving in terms of sophistication, and developing reliable detection methods is now more crucial than ever. This study provides a comprehensive literature review of current AI-based deepfake detection techniques, examining their strengths, limitations, and real-world performance. We reviewed existing methods’ challenges, especially in dealing with complex manipulations, diverse datasets, and low-quality video data. Building upon this analysis, the research focuses on enhancing the AltFreezing method, a respected approach for detecting spatial and temporal artifacts in video forgeries. This approach leverages discrepancies in motion and texture introduced during the deepfake generation process. To improve its effectiveness, preprocessing t echniques a re introduced in the detection pipeline. Methods such as noise reduction, contrast adjustment, and sharpening are explored to refine t he i nput d ata, p otentially b oosting t he s ystem’s a bility t o detect subtle manipulation artifacts. This integration was aimed at strengthening the performance of AltFreezing, especially in challenging scenarios involving low-resolution content, varying lighting conditions, or sophisticated forgeries that employ anti-detection techniques. Preliminary findings indicate t hat i ncorporating preprocessing s teps may enhance the overall robustness and accuracy of face forgery detection systems. These insights contribute to ongoing research and development in the field of deepfake detection, offering a promising direction f or f uture advancements in handling complex and diverse real-world situations. The enhanced AltFreezing method. Preprocessing seems to have the potential to become a vital tool for media platforms, law enforcement, and cybersecurity professionals in the fight against malicious deepfake content.