Counter-forensics of SIFT-based copy-move detection by means of keypoint classification

Abstract

Copy-move forgeries are very common image manipulations that are often carried out with malicious intents. Among the techniques devised by the 'Image Forensic' community, those relying on scale invariant feature transform (SIFT) features are the most effective ones. In this paper, we approach the copy-move scenario from the perspective of an attacker whose goal is to remove such features. The attacks conceived so far against SIFT-based forensic techniques implicitly assume that all SIFT keypoints have similar properties. On the contrary, we base our attacking strategy on the observation that it is possible to classify them in different typologies. Also, one may devise attacks tailored to each specific SIFT class, thus improving the performance in terms of removal rate and visual quality. To validate our ideas, we propose to use a SIFT classification scheme based on the gray scale histogram of the neighborhood of SIFT keypoints. Once the classification is performed, we then attack the different classes by means of class-specific methods. Our experiments lead to three interesting results: (1) there is a significant advantage in using SIFT classification, (2) the classification-based attack is robust against different SIFT implementations, and (3) we are able to impair a state-of-the-art SIFT-based copy-move detector in realistic cases.

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

“…This consideration was because of the most recent malevolent exercises in which a particular object is copied on the same image. These types of activities can be seen in the case of copy-move image forgery that considers a stands out amongst the most known type of activity which focuses on adding or concealing an object [13], [14]. Numerous scholars have decided that copy-move image forgery works on the grounds of detecting additional noise, texture and color changes and these may be observed in the duplicated area of the image.…”

Video Forgery detection using Hybrid techniques

“…SIFT [2] can robustly identify objects even among clutter and under partial occlusion, because the SIFT feature descriptor is invariant to uniform scaling, orientation, and partially invariant to affine distortion and illumination changes [1].This section summarizes Lowe's object recognition method and mentions a few competing techniques available for object recognition under clutter and partial occlusion. SIFT key points of objects are first extracted from a set of reference images [1] and stored in a database.…”

“…The algorithm was published by David Lowe in 1999 [1].For any object in an image, interesting points on the object can be extracted to provide a "feature description" of the object. This description, extracted from a training image, can then be used to identify the object when attempting to locate the object in a test image containing many other objects.…”

“…are assuming a critical part. Other than this, different applications like amusement industry, video observation, lawful confirmation, political recordings, video instructional exercises, commercials, and so on mean their uncommon part in today's connection [1].…”

“…SIFT key points of objects are first extracted from a set of reference images [1] and stored in a database. An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors.…”