Image Forgery Detection using Convolutional Neural Networks and Blockchain Technology

In the era of digital media, the growth of picture forgeries poses a huge threat to content validity and reliability. Previous techniques for picture forgery detection have found constraints in accuracy and scalability, forcing the creation of more robust systems. This article suggests a unique technique that merges convolutional neural networks (CNNs) and blockchain technology to address this critical issue. Leveraging a carefully curated dataset combining authentic and forged photos generated through various manipulation techniques, we trained our algorithm to detect and categorize forged content with high accuracy. Our test outcomes revealed impressive performance, with the highest achievable accuracy, precision, recall, and F1-score values standing at 0.95, 0.92, 0.94, and 0.93, respectively. This method represents a considerable leap in image forgery detection and shows promise for fighting digital tampering in varied real-world applications.