Advance Image Forgery Detection

Abstract :- In the digital era, the proliferation of sophisticated image editing tools has made image forgery a prevalent issue, posing significant challenges in various fields, including legal proceedings, journalism, and digital security. Advanced image forgery detection techniques are crucial to ensuring the authenticity and integrity of digital images. This paper explores the state-of-the-art methods and technologies employed in the detection of image forgeries. We focus on both traditional and deep learning-based approaches, highlighting their respective strengths and weaknesses. Traditional methods, such as pixel-based, format-based, and geometry-based techniques, rely on the intrinsic properties of images to identify inconsistencies and manipulations. These methods are effective in detecting simple forgeries but often fall short against more sophisticated alterations. On the other hand, deep learning-based approaches, leveraging convolutional neural networks (CNNs) and generative adversarial networks (GANs), have shown significant promise in identifying subtle and complex forgeries by learning high-level features from large datasets. In conclusion, while significant advancements have been made in image forgery detection, continuous research and development are necessary to stay ahead of emerging forgery techniques. The integration of multidisciplinary approaches and the collaboration between academia and industry will be pivotal in advancing the efficacy of forgery detection systems. Keywords : Image Forgery Detection, Deep Learning, Digital Image Authentication, Pixel-based Methods, Image Manipulation. Image Manipulation.