TY - JOUR AU - Chandan deep Kaur AU - Navdeep Kanwal PY - 2019/05/19 Y2 - 2024/03/28 TI - An Analysis of Image Forgery Detection Techniques JF - Statistics, Optimization & Information Computing JA - Stat., optim. inf. comput. VL - 7 IS - 2 SE - Research Articles DO - 10.19139/soic.v7i2.542 UR - http://www.iapress.org/index.php/soic/article/view/soic.190617 AB - Society is becoming increasingly dependent on the internet and so does it become more and more vulnerable to harmful threats. These threats are becoming vigorous and continuously evolving. These threats distorted the authenticity of data transmitted through the internet. As we all completely or partially rely upon this transmitted data. Hence, its authenticity needs to be preserved. Images have the potential of conveying much more information as compared to the textual content. We pretty much believe everything that we see. In order to preserve/check the authenticity of images, image forgery detection techniques are expanding its domain. Detection of forgeries in digital images is in great need in order to recover the people’s trust in visual media. This paper is going to discuss different types of image forgery and blind methods for image forgery detection. It provides the comparative tables of various types of techniques to detect image forgery. It also gives an overview of different datasets used in various approaches of forgery detection. ER -