Detecting Image Brush Editing Using the Discarded Coefficients and Intentions

This paper describes a quick and simple method to detect brush editing in JPEG images. The novelty of the proposed method is based on detecting the discarded coefficients during the quantization of the image. Another novelty of this paper is the development of a subjective metric named intentions. The method directly analyzes the allegedly tampered image and generates a forgery mask indicating forgery evidence for each image block. The experiments show that our method works especially well in detecting brush strokes, and it works reasonably well with added captions and image splicing. However, the method is less effective detecting copy-moved and blurred regions. This means that our method can effectively contribute to implementing a complete imagetampering detection tool. The editing operations for which our method is less effective can be complemented with methods more adequate to detect them

Residue properties for the arithmetical estimation of the image quantization table

Ttaditionally, a statistical approach has been used to detect the JPEG quantization table used to compressa bitmap. This approach has the disadvantage that at times false solutions are found. These false solutions may have important implications if, for example, a court expert issues an incorrect assessment on whether an image is forged. This paper develops the concept of residue properties, which enables us to determine the quantization table following an arithmetic approach. This study shows that these properties allow us to ensure that no false solutions are produced, but at the cost of being able to obtain more than one compatible solution. Sometimes we prefer to find this set of possible Q values (quantization values) used, without risking obtaining a false solution. If we choose to obtain a unique answer for Q, then we can perform a statistical analysis on this pruned space of compatible solutions to decide the most probable Q value. In this way, a higher success rate is obtained than if we perform only a statistical soft computing analysis on the total space of solutions. (C) 2018 Elsevier B.V. All rights reserved