State-of-the-art techniques for passive image forgery detection: a brief review. (14th July 2022) - Kaur, Simranjot; Rani, Rajneesh; Garg, Ritu; Sharma, Nonita
Images are major information carriers in the digital era. Along with the benefits, there are many drawbacks of digital visual media. The digital multimedia editing tools like Adobe Photoshop, CorelDRAW, Affinity, Freehand, GNU Image Manipulation Program (GIMP), etc. are being used to tamper or manipulate the images for malicious purposes. Image forgery is the process of manipulating a digital image by adding some content or hiding some content such that the integrity of the image is lost. So, it becomes important to check the credibility and integrity of the images. In order to detect the image manipulation, various active and passive techniques have been put forward. The recent methods make use of deep learning techniques to detect image tampering. This manuscript attempts to review state-of-the-art approaches in the discipline of passive image forgery detection, and presents a comparative performance analysis. Also, the publicly available benchmarking databases for image forgery detection and performance evaluation parameters are elucidated.