Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch

The recently developed deep algorithms achieve promising progress in the field of image copy-move forgery detection (CMFD). However, they have limited generalizability in some practical scenarios, where the copy-move objects may not appear in the training images or cloned regions are from the background. To address the above issues, in this work, we propose a novel end-to-end CMFD framework by integrating merits from both conventional and deep methods. Specifically, we design a deep cross-scale patchmatch method tailored for CMFD to localize copy-move regions. In contrast to existing deep models, our scheme aims to seek explicit and reliable point-to-point matching between source and target regions using features extracted from high-resolution scales. Further, we develop a manipulation region location branch for source/target separation. The proposed CMFD framework is completely differentiable and can be trained in an end-to-end manner. Extensive experimental results demonstrate the high generalizability of our method to different copy-move contents, and the proposed scheme achieves significantly better performance than existing approaches.Comment: 6 pages, 4 figures, accepted by ICME202

Copy-move forgery detection using the segment gradient orientation histogram

The ready availability of image-editing software makes ensuring the authenticity of images an important issue. The most common type of image tampering is cloning, or Copy-Move Forgery (CMF), in which part(s) of the image are copied and pasted back into the same image. One possible transformation is where an object is copied, rotated and pasted; this type of forgery is called Copy-Rotate-Move Forgery (CRMF). Applying post-processing can be used to produce more realistic doctored images and thus can increase the difficulty of forgery detection. This paper presents a novel segmentation-based Copy-Move forgery detection method. A new method has been developed to segment the Copy-Move objects in a consistent way that is more efficient than Simple Linear Iterative Clustering (SLIC) segmentation for CMF/CRMF. We propose a new method to describe irregular shaped blocks (segments). The Segment Gradient Orientation Histogram (SGOH), is used to describe the gradient distribution of each segment. The quality of initial matches is improved by applying hysteresis to grow the primary detection regions. We show that the proposed method can effectively detect forgery involving translation and rotation. Moreover, the proposed method can detect forgery in images with blurring, brightness change, colour reduction, JPEG compression, variations in contrast and added noise

Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy-clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets

Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection

Copy-move forgery is a manipulation of copying and pasting specific patches from and to an image, with potentially illegal or unethical uses. Recent advances in the forensic methods for copy-move forgery have shown increasing success in detection accuracy and robustness. However, for images with high self-similarity or strong signal corruption, the existing algorithms often exhibit inefficient processes and unreliable results. This is mainly due to the inherent semantic gap between low-level visual representation and high-level semantic concept. In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation. Our detection method expands the traditional works on two aspects: 1) we introduce the bag-of-visual-words model into this field for the first time, may meaning a new perspective of forensic study; 2) we propose a word-to-phrase feature description and matching pipeline, covering the spatial structure and visual saliency information of digital images. Extensive experimental results show the superior performance of our framework over state-of-the-art algorithms in overcoming the related problems caused by the semantic gap.Comment: 13 pages, 11 figure

MARKABLE CONTROLLED WATERSHED SEGMENTATION BASED FORGERY DETECTION USING FEATURE POINT MATCHING ALGORITHM

A novel duplicate copy move fraud identification scheme based on Markable Controlled Watershed Segmentation and feature point matching. The current project incorporates the novelty of marker controlled watershed segmentation and also feature point matching techniques. To begin with, the arranged marker controlled Over-Segmentation equation sections the host picture into non-covering and sporadic squares adaptively. At that point, the component focuses are separated from each piece as square alternatives, and furthermore the piece choices are coordinated with each other to locate the forgery based areas; this strategy will show better results compared to the adaptive over segmentation technique as it reduces the computational complexity of the process but selectively segmenting the image based on markers generated during the segmentation process. To watch the falsification areas extra precisely, we propose the marker controlled segmentation, that replaces the component focuses with minimal super pixels as highlight pieces at that point combines the neighboring hinders that have comparable local shading choices into the element squares to think of the coordinated districts; at last, It uses the morphological techniques to an incorporated area to concoct the recognized phony locales