Copy-move Image Forgery Detection Research Articles

Deep Learning Methods for Copy Move Image Forgery Detection: A Review

Deep Learning Methods for Copy Move Image Forgery Detection: A Review

A survey on copy-move image forgery detection based on deep-learning techniques

A survey on copy-move image forgery detection based on deep-learning techniques

Cascaded Adaptive Graph Representation Learning for Image Copy-Move Forgery Detection

In the realm of image security, there has been a burgeoning interest in harnessing deep learning techniques for the detection of digital image copy-move forgeries, resulting in promising outcomes. The generation process of such forgeries results in a distinctive topological structure among patches, and collaborative modeling based on these underlying topologies proves instrumental in enhancing the discrimination of ambiguous pixels. Despite the attention received, existing deep learning models predominantly rely on convolutional neural networks (CNNs), falling short in adequately capturing correlations among distant patches. This limitation impedes the seamless propagation of information and collaborative learning across related patches. To address this gap, our work introduces an innovative framework for image copy-move forensics rooted in graph representation learning. Initially, we introduce an adaptive graph learning approach to foster collaboration among related patches, dynamically learning the inherent topology of patches. The devised approach excels in promoting efficient information flow among related patches, encompassing both short-range and long-range correlations. Additionally, we formulate a cascaded graph learning framework, progressively refining patch representations and disseminating information to broader correlated patches based on their updated topologies. Finally, we propose a hierarchical cross-attention mechanism facilitating the exchange of information between the cascaded graph learning branch and a dedicated forgery detection branch. This equips our method with the capability to jointly grasp the homology of copy-move correspondences and identify inconsistencies between the target region and the background. Comprehensive experimental results validate the superiority of our proposed scheme, providing a robust solution to security challenges posed by digital image manipulations.

Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning.

Recent advances in deep learning algorithms have shown impressive progress in image copy-move forgery detection (CMFD). However, these algorithms lack generalizability in practical scenarios where the copied regions are not present in the training images, or the cloned regions are part of the background. Additionally, these algorithms utilize convolution operations to distinguish source and target regions, leading to unsatisfactory results when the target regions blend well with the background. To address these limitations, this study proposes a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods. Specifically, the study develops a deep cross-scale PatchMatch (PM) method that is customized for CMFD to locate copy-move regions. Unlike existing deep models, our approach utilizes features extracted from high-resolution scales to seek explicit and reliable point-to-point matching between source and target regions. Furthermore, we propose a novel pairwise rank learning framework to separate source and target regions. By leveraging the strong prior of point-to-point matches, the framework can identify subtle differences and effectively discriminate between source and target regions, even when the target regions blend well with the background. Our framework is fully differentiable and can be trained end-to-end. Comprehensive experimental results highlight the remarkable generalizability of our scheme across various copy-move scenarios, significantly outperforming existing methods.

Lightweight and High-Precision Network for Image Copy-Move Forgery Detection

Lightweight and High-Precision Network for Image Copy-Move Forgery Detection

Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

Modeling of intelligent hyperparameter tuned deep learning based copy move image forgery detection technique

Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters.

A hybrid SWT-SVD based multiresolution features for robust image copy-move forgery detection

A hybrid SWT-SVD based multiresolution features for robust image copy-move forgery detection

Survey on image copy-move forgery detection

Survey on image copy-move forgery detection

SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection.

With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods.

Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering

Image copy-move forgery is a common simple tampering technique. To address issues such as high time complexity in most copy-move forgery detection algorithms and difficulty detecting forgeries in smooth regions, this paper proposes an image copy-move forgery detection algorithm based on fused features and density clustering. Firstly, the algorithm combines two detection methods, speeded up robust features (SURF) and accelerated KAZE (A-KAZE), to extract descriptive features by setting a low contrast threshold. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm removes mismatched pairs and reduces false positives. To improve the accuracy of forgery localization, the algorithm uses the original image and the image transformed by the affine matrix to compare similarities in the same position in order to locate the forged region. The proposed method was tested on two datasets (Ardizzone and CoMoFoD). The experimental results show that the method effectively improved the accuracy of forgery detection in smooth regions, reduced computational complexity, and exhibited strong robustness against post-processing operations such as rotation, scaling, and noise addition.

Convolutional block attention based network for copy-move image forgery detection

Convolutional block attention based network for copy-move image forgery detection

Accurate and robust image copy-move forgery detection using adaptive keypoints and FQGPCET-GLCM feature

Accurate and robust image copy-move forgery detection using adaptive keypoints and FQGPCET-GLCM feature

A fast copy-move image forgery detection approach on a reduced search space

A fast copy-move image forgery detection approach on a reduced search space

Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection

Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection

Retracted: Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection.

[This retracts the article DOI: 10.1155/2022/8501738.].

ERINet: efficient and robust identification network for image copy-move forgery detection and localization

ERINet: efficient and robust identification network for image copy-move forgery detection and localization

Image copy-move forgery detection based on dynamic threshold with dense points

Copy-move tampering is one of the most popular tampering techniques at present. The tampered region of the image has good fusion with the original image, which increases the difficulty of detection. After years of research, the current detection method based on key points still has the following problems: 1) Failure to achieve forgery detection of small areas/self-similar areas/smooth areas, 2) Lack of reasonable feature point extraction methods, 3) The various stages of copy-move forgery detection (CMFD) work are relatively independent and lack close connections, 4) A fixed threshold is used as the region of interest similarity metric in the matching and localization stages. The failure to consider tampered images and the diversity of tampered regions leads to the limited detection capability of the algorithm. Considering the actual situation of tampering with the picture, to solve the above problems, we propose a copy-move forgery detection method based on the dynamic threshold. First, we determine the point extraction strategy in each super pixel block according to the size of the simple line interface calculation (SLIC) super pixel block and the Weber local descriptor (WLD) descriptor to ensure the reasonable allocation of feature points and reduce unnecessary points. These key points are then characterized by the scaling, flip and rotation invariants of the fractional general Jacobi-Fourier moments (FJFMs). Then, the matching and mismatch filtering thresholds of each feature point are determined through the WLD and SLIC features, and the SLIC feature is used to replace the distance threshold to improve the detection accuracy of small manufacturing areas. Finally, based on the matching results and SLIC features, an effective positioning method is proposed to improve the speed and accuracy of positioning. Experimental results show that the proposed algorithm is superior to the classic methods in recent years in terms of time and accuracy.

QDL-CMFD: A Quality-independent and deep Learning-based Copy-Move image forgery detection method

One of the prevalent methods of image forgery is copy-move, where one or more regions of an image are duplicated and moved elsewhere in the image. It is usually difficult to detect this type of forgery due to the similarity of the copied and forged areas. Also, forgers perform pre-processing and/or post-processing operations on the manipulated regions to make it even more difficult to detect. In this study, an image quality-independent method based on deep learning approach, termed QDL-CMFD, is presented for detecting this type of forgery. QDL-CMFD utilizes generative adversarial networks for image quality enhancement, and convolutional neural networks (CNN) for forgery detection. A tailored dual-branch CNN architecture is introduced consisting of two subnetworks, namely a manipulation detection subnetwork and a similarity detection subnetwork. Accordingly, unlike most existing methods, QDL-CMFD is able to simultaneous detection of several forged areas, as well as determining the source and target of the forgery. Also, QDL-CMFD is robust against various pre-processing/post-processing attacks. It shows excellent performance for detecting low-quality forged images and small areas. Experiments conducted on the CASIA and CoMoFoD benchmark datasets confirm that QDL-CMFD performs significantly better than the competitors. All the implementation source codes of QDL-CMFD are available at https://github.com/MehradAria/QDL-CMFD.

Image copy-move forgery detection and localization based on super-BPD segmentation and DCNN

With the increasing importance of image information, image forgery seriously threatens the security of image content. Copy-move forgery detection (CMFD) is a greater challenge because its abnormality is smaller than other forgeries. To solve the problem that the detection results of the most image CMFD based on convolutional neural networks (CNN) have relatively low accuracy, an image copy-move forgery detection and localization based on super boundary-to-pixel direction (super-BPD) segmentation and deep CNN (DCNN) is proposed: SD-Net. Firstly, the segmentation technology is used to enhance the connection between the same or similar image blocks, improving the detection accuracy. Secondly, DCNN is used to extract image features, replacing conventional hand-crafted features with automatic learning features. The feature pyramid is used to improve the robustness to the scaling attack. Thirdly, the image BPD information is used to optimize the edges of rough detected image and obtain final detected image. The experiments proved that the SD-Net could detect and locate multiple, rotated, and scaling forgery well, especially large-level scaling forgery. Compared with other methods, the SD-Net is more accurately located and robust to various post-processing operations: brightness change, contrast adjustments, color reduction, image blurring, JPEG compression, and noise adding.