Fake Product Identification Using Deep Learning

Digital images play a critical role across various domains, including journalism, criminal forensics, and medical imaging. However, the growing availability of advanced image editing tools has raised concerns regarding the authenticity of digital photographs, particularly in contexts where integrity is paramount. Image forgery, a predominant form of manipulation, involves altering or concealing key image details by copying and pasting portions of the image, often without visible signs of tampering. Such manipulations pose significant challenges for digital forensics in validating image authenticity. This poses significant challenges for digital forensics in authenticating images. To address this, we propose a deep learning-based approach for detecting image forgeries. Using a Convolutional Neural Network (CNN), the method extracts image features and classifies them as authentic or manipulated. The proposed approach addresses the growing sophistication of digital forgery techniques, offering enhanced accuracy and reliability in image authentication and contributing to the advancement of forensic analysis.