Video And Image Manipulation Detection Using Multiple Deep Learning Techniques

Computer Engineering

Video and image manipulation, such as deepfakes, and AI-generated and computer-generated media, has become a major concern in recent years due to the advancements in technologies. With the help of these, it is possible for misusing visual media for malicious purposes. Detecting manipulation in videos and images is crucial for various applications, including forensics, journalism, and social media. In this paper, we address the problem of detecting manipulated and generated videos and images using deep learning techniques, with a focus on methods relevant to forensic analysis. We explore and analyze existing methods and technologies for detecting deepfakes and CGI, manipulations, and develop and test novel approaches to arrive at a robust and effective solution. Our evaluation of the performance of different algorithms demonstrates the feasibility and effectiveness of our proposed methods in detecting various types of manipulations. The results of this project have important implications for forensic analysis and other applications where the authenticity of visual media is crucial. The development of reliable methods for detecting manipulations using deep learning techniques can help ensure the credibility and trustworthiness of visual media in various contexts.

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