Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks

In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and the segmentation-based multi-scale analysis to locate tampered areas in digital images. First, to deal with color input sliding windows of different scales, a unified CNN architecture is designed. Then, we elaborately design the training procedures of CNNs on sampled training patches. With a set of robust multi-scale tampering detectors based on CNNs, complementary tampering possibility maps can be generated. Last but not least, a segmentation-based method is proposed to fuse the maps and generate the final decision map. By exploiting the benefits of both the small-scale and large-scale analyses, the segmentation-based multi-scale analysis can lead to a performance leap in forgery localization of CNNs. Numerous experiments are conducted to demonstrate the effectiveness and efficiency of our method.Comment: 7 pages, 6 figure

Camera-based Image Forgery Localization using Convolutional Neural Networks

Camera fingerprints are precious tools for a number of image forensics tasks. A well-known example is the photo response non-uniformity (PRNU) noise pattern, a powerful device fingerprint. Here, to address the image forgery localization problem, we rely on noiseprint, a recently proposed CNN-based camera model fingerprint. The CNN is trained to minimize the distance between same-model patches, and maximize the distance otherwise. As a result, the noiseprint accounts for model-related artifacts just like the PRNU accounts for device-related non-uniformities. However, unlike the PRNU, it is only mildly affected by residuals of high-level scene content. The experiments show that the proposed noiseprint-based forgery localization method improves over the PRNU-based reference

Improved Tampering Localization in Digital image Forensics: Comparative Study Based on Maximal Entropy Random Walk and Multi-Scale Fusion

Nowadays the increasing ease of editing digital photographs has spawned an urgent need for reliable authentication mechanism capable of precise localization of potential malicious forgeries. In this paper we compare two different Techniques to analyze which technique can be used more efficiently in localization of Tampered Region In Digital Image .First Technique is Maximal Entropy Random Walk in which Strong localization property of this random walk will highlight important regions and to diminish the background- even for noisy response maps. Our evaluation will show that the proposed method can significantly perform both the commonly used threshold-based decision, and the recently proposed optimization approach with a Markovian prior. The second Technique which is based on Multi-Scale Fusion will investigate a multi-scale analysis approach which merge multiple candidate tampering maps, obtained from the analysis with different windows, to obtain a single, more efficient tampering map with better localization resolution. We propose three different techniques for multi- scale fusion, and verify their feasibility .In this slant we consider popular tampering scenario to distinguish between singly and doubly compressed region