Keypoints-Based Image Passive Forensics Method for Copy-Move Attacks

A novel image passive forensics method for copy-move forgery detection is proposed. The proposed method combines block matching technology and feature point matching technology, and breaks away from the general framework of the visual feature-based approach that used local visual feature such as SIFT and followed by a clustering procedure to group feature points that are spatially close. In our work, image keypoints are extracted using Harris detector, and the statistical features of keypoint neighborhoods are used to generate forensics features. Then we proposed a new forensics features matching approach, in which, a region growth technology and a mismatch checking approach are developed to reduce mismatched keypoints and improve detected accuracy. We also develop a duplicate region detection method based on the distance frequency of corresponding keypoint pairs. The proposed method can detect duplicate regions for high resolution images. It has higher detection accuracy and computation efficiency. Experimental results show that the proposed method is robust for content-preserving manipulations such as JPEG compression, gamma adjustment, filtering, luminance enhancement, blurring, etc.

Anti-Forensics for Unsharp Masking Sharpening in Digital Images

As a retouching tool, image sharpening can be applied as the final step to hide those possible forgery operations in an image. Unsharp masking (USM) is a popular sharpening method supported by most image editing software such as Adobe Photoshop. Several passive forensics methods have been presented for the detection of USM Sharpening. In this paper, an anti-forensic scheme for USM Sharpening is proposed to invalidate the existing forensic algorithms. It removes the overshoot artifacts in image edges and abrupt change in histogram ends. The effectiveness of the proposed method is proved by the experimental results on a large image database with various parameter settings. Comparisons are made among the unsharpened images, the sharpened images and the anti-forensic dithered image. Both the detection ability and image quality are used for its performance evaluation.

Passive Forensics for Region Duplication Image Forgery Based on Harris Feature Points and Local Binary Patterns

Nowadays the demand for identifying the authenticity of an image is much increased since advanced image editing software packages are widely used. Region duplication forgery is one of the most common and immediate tampering attacks which are frequently used. Several methods to expose this forgery have been developed to detect and locate the tampered region, while most methods do fail when the duplicated region undergoes rotation or flipping before being pasted. In this paper, an efficient method based on Harris feature points and local binary patterns is proposed. First, the image is filtered with a pixelwise adaptive Wiener method, and then dense Harris feature points are employed in order to obtain a sufficient number of feature points with approximately uniform distribution. Feature vectors for a circle patch around each feature point are extracted using local binary pattern operators, and the similar Harris points are matched based on their representation feature vectors using the BBF algorithm. Finally, RANSAC algorithm is employed to eliminate the possible erroneous matches. Experiment results demonstrate that the proposed method can effectively detect region duplication forgery, even when an image was distorted by rotation, flipping, blurring, AWGN, JPEG compression, and their mixed operations, especially resistant to the forgery with the flat area of little visual structures.