Abstract:

The proliferation of digital images and advanced editing tools has raised concerns over image authenticity. Image Forgery presents an automated approach to detect digital manipulations using Convolutional Neural Networks (CNN). By analyzing intrinsic patterns and anomalies in images, the model distinguishes between authentic and forged images with high accuracy. The system leverages multiple layers of CNN to extract deep features indicative of tampering. Experimental results demonstrate the robustness of the approach across various manipulation techniques, including splicing, copy-move, and retouching. This research provides a foundation for reliable digital forensics in combating misinformation and image-based fraud.

Description:

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