Copy-move Forgery Detection Technique Research Articles

A copy-move forgery detection technique using DBSCAN-based keypoint similarity matching

A copy-move forgery detection technique using DBSCAN-based keypoint similarity matching

Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction

Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction

Altered Region Identification in Image Files using Copy-Move Forgery Detection Technique

Nowadays, digital images and videos have high importance because they have become the primary carriers of information. However, the easy availability of powerful image editing software has made it possible to manipulate and edit digital images and videos, leading to a loss of trust in their authenticity. It is possible to add or remove important features from an image without leaving any obvious traces of tampering. To detect this such type of forgeries, the proposed method involves dividing the image into overlapping blocks of the same size, extracting a feature for each block, and representing it as a vector. The vectors are then sorted using radix sort, and the difference in the positions of adjacent feature vectors in the sorted list is computed to obtain a shift vector. This method can even identify tampered parts of an image that have been enhanced or retouched to merge with the background or saved in a lossy format like JPEG. Several forged images were tested to demonstrate the effectiveness of this proposed method. Its application has significant implications in fields like forensics, journalism, and medical imaging to ensure the authenticity of digital images and videos.

Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content. An effective technique for tampering the identification is the copy-move forgery. Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification. Contrastingly, deep learning (DL) models have demonstrated significant performance over the other statistical techniques. With this motivation, this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection (ODTL-CMFD) technique. The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered, and then localize the copy moved regions. To perform the feature extraction process, the political optimizer (PO) with Mobile Networks (MobileNet) model has been derived for generating a set of useful vectors. Finally, an enhanced bird swarm algorithm (EBSA) with least square support vector machine (LS-SVM) model has been employed for classifying the digital images into the original or the forged ones. The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine (MSVM) technique and thereby enhance the classification performance. For ensuring the enhanced performance of the ODTL-CMFD technique, a series of simulations have been performed against the benchmark MICC-F220, MICC-F2000, and MICC-F600 datasets. The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.

A new Copy-Move forgery detection method using LIOP

The most prevalent type of digital image falsification occurs when a portion of a image is copied and pasted onto another section of the same image. Falsification of the image made in this way is called copy-move forgery (CMF). This study presents a new and effective approach for copy-move forgery detection (CMFD) using the Local Intensity Order Pattern (LIOP) to overcome the restrictions of existing CMFD techniques. The input image is first converted to a YCbCr color space and then split into Y, Cb, and Cr color channels. The LIOP features are then extracted from each color channel and all the features are combined. The feature vectors are ordered lexicographically and related features are detected by comparing the LIOP features. Although the LIOP feature has rarely been used in CMFD prior to this study, the success rate of the proposed method is high. In addition, since the channels are not correlated to each other in the YCbCr color space, each color channel is considered as a gray image, and the success rate is increased by combining the features extracted from each of the color channels. The proposed approach was assessed using the CoMoFoD and GRIP datasets. Experimental findings demonstrated that the suggested method was successful and displayed robustness in post-processing attacks.

FAST, BRIEF and SIFT based image copy-move forgery detection technique

FAST, BRIEF and SIFT based image copy-move forgery detection technique

Detection and Localization of Copy-Move Forgery in Digital Images: Review and Challenges

Copy move forgery in digital images became a common problem due to the wide accessibility of image processing algorithms and open-source editing software. The human visual system cannot identify the traces of forgery in the tampered image. The proliferation of such digital images through the internet and social media is possible with a finger touch. These tampered images have been used in news reports, judicial forensics, medical records, and financial statements. In this paper, a detailed review has been carried on various copy-move forgery detection (CMFD) and localization techniques. Further, challenges in the research are identified along with possible solutions.

An improved reduced feature-based copy-move forgery detection technique

An improved reduced feature-based copy-move forgery detection technique

Comparative Study of Recent CMFD Techniques: A Review

Due to the ease in manipulation of digital images with various existing photo editing tools and softwares the task of authentication and identifying the reliability of digital image has become a major concern. Copy-move forgery detection (CMFD) is therefore known to be one of the key-domains in the latest digital image authentication study. Copy-move forgery is a passive approach in which a portion of an image is copied and then pasted onto the same image, resulting in a tampered image. In this paper, current developments in CMFD have been surveyed and a comparative evaluation of recent CMFD techniques has been done along with its advantages and limitations. Also, a detailed description of relevant copy-move forgery detection datasets is provided which will help the researchers to decide which dataset to choose for a given CMFD approach.

Robust copy-move forgery detection using modified superpixel based FCM clustering with emperor penguin optimization and block feature matching

Digital images face widespread tampering through a copy-move attack, wherein objects are added or removed from an image to create the copy-move forged image. This paper proposes a robust Copy Move Forgery Detection (CMFD) scheme by integrating both block-based and keypoint-based CMFD techniques. The input image is first segmented into non-overlapping blocks using a modified FCM clustering algorithm based on superpixels. The neighboring and similar superpixels influences are encompassed into FCM, and Emperor Penguin Optimization (EPO) is used to optimize the influential degree, which increases the segmentation performance. Gabor filter is applied to extract the features from each block. The Block features are then matched with each other to show the suspected forgery regions. The experimental results illustrate that the proposed technique attains better performance in terms of precision, recall, F1-score, and E-measure. It accomplishes excellent forgery detection outcomes compared with other existing CMFD processes, even under different perplexing circumstances.

Improved copy move forgery detection method via L*a*b* color space and enhanced localization technique

The wide availability of easy-to-use image editors has made the authenticity of images questionable. Copy-move is one of the most applied forgery types. A new copy-move forgery detection and localization technique independent from the characteristics of the forged regions is proposed in this paper. SIFT keypoints are obtained from CLAHE applied sub-images extracted from the input image by using RGB and L*a*b* color-spaces. Keypoint matching is realized on the sub-images and duplicated regions are determined roughly to create roughly marked image R. RANSAC is also applied in this stage and generated homography matrix is used to construct transformed roughly marked image R′. The method extracts DCT based features from R and R′ to localize exact borders of the tampered regions on the roughly determined areas by using a dynamic threshold. The proposed method has a new suggestion to determine the threshold dynamically. Tamper localization procedure also utilizes from morphological operations (chosen depending on the characteristic of the image) and Connected Component Labeling to determine exact forge boundaries. Results indicate that the proposed method has a better performance compared with state-of-the-art copy-move forgery detection methods on the GRIP dataset. Scaling attack performance of the method is especially better than similar works as shown in the results.

Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

Digital Image Forensics is a growing field of image processing that attempts to gain objective ‎proof ‎of the origin and veracity of a visual image. Copy-move forgery detection (CMFD) has ‎currently ‎become an active research topic in the passive/blind image forensics field. There has no ‎doubt that ‎conventional techniques and especially the keypoint based techniques have pushed the ‎CMFD ‎forward in the previous two decades. However, CMFD techniques in general and ‎conventional ‎techniques in particular suffer from several challenges. And thus, increasing approaches ‎are exploiting ‎deep learning for CMFD. In this survey, we cover the conventional and the ‎deep learning ‎based CMFD techniques from a new perspective. We classify the ‎CMFD techniques into several ‎classifications according to the detection methodology, the detection paradigm, and the detection ‎capability‎. We discuss the ‎challenges facing the CMFD techniques as well as the ways for solving ‎them. In addition, this survey covers the evaluation metrics‎ and datasets commonly utilized for ‎CMFD. Also, we are ‎debating and proposing certain plans for future research. This survey will be ‎helpful for the researchers’ ‎as it master the recent trends of CMFD and outline some future research ‎directions.‎

Copy-move forgery detection technique based on discrete cosine transform blocks features

With the increasing number of software applications that allow altering digital images and their ease of use, they weaken the credibility of an image. This problem, together with the ease of distributing information through the Internet (blogs, social networks, etc.), has led to a tendency for information to be accepted as true without its veracity being questioned. Image counterfeiting has become a major threat to the credibility of the information. To deal with this threat, forensic image analysis is aimed at detecting and locating image forgeries using multiple clues that allows it to determine the veracity or otherwise of an image. In this paper, we present a method for the authentication of images. The proposed method performs detection of copy-move alterations within an image, using the discrete cosine transform. The characteristics obtained from these coefficients allow us to obtain transfer vectors, which are grouped together. Through the use of a tolerance threshold, it is possible to determine whether there are regions copied and pasted within the analysed image. The results obtained from the experiments reported in this paper demonstrate the effectiveness of the proposed method. For the evaluation of the proposed methods, experiments were carried out with public databases of falsified images that are widely used in the literature.

A Survey Paper on Image forgery detection Using Pseudo Zernike Moment

Photographs are taken as valid evidences in various scenarios of our day to day life. Because of the developments in the field of Image Processing, altering images according to ones need is not a difficult task. Techniques of Image Forensics play its crucial role at this juncture. One of the mostly found types of image tampering is Copy-Move forgery. A copy-move forgery is performed by copying a region in an image and pasting it on another region in the same image, mostly after some form of post-processing like rotation, scaling, blurring, noise addition, JPEG compression etc. Two types of copy-move forgery detection techniques exist in literature. They are the Block based methods and Key-point based methods. Both the methods have their own advantages and limitations. This paper presents a survey on the recent developments in block based methods. As forgeries have become popular, the importance of forgery detection is much increased. Copy-move forgery, one of the most commonly used methods, copies a part of the image and pastes it into another part of the image. In this paper, we propose a detection method of copy-move forgery that localizes duplicated regions using Zernike moments. Since the magnitude of Zernike moments is algebraically invariant against rotation, the proposed method can detect a forged region even though it is rotated. Our scheme is also resilient to the intentional distortions such as additive white Gaussian noise, JPEG compression, and blurring. Experimental results demonstrate that the proposed scheme is appropriate to identify the forged region by copy-rotate-move forgery.

Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.

A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata

Copy Move Forgery (CMF) is a type of digital image forgery in which an image region is copied and pasted to another location within the same image with malicious intent to misrepresent its meaning. To prevent misinterpretation of an image content, several Copy Move Forgery Detection (CMFD) methods have been proposed in the past. However, the existing methods show limited robustness on images altered with post-processing attacks such as noise addition, compression, blurring etc. In this paper, we propose a robust method for detecting copy-move forgeries under different post-processing attacks. We use Discrete Cosine Transform (DCT) to extract features from each block. Next, Cellular Automata is employed to construct feature vectors based on the sign information of the DCT coefficients. Finally, feature vectors are matched using the kd-tree based nearest-neighbor searching method to find the duplicated areas in the image. Experimental results show that the proposed method performs exceptionally well relative to the other state-of-the-art methods from the literature even when an image is heavily affected by the post-processing attacks, in particular, JPEG compression and additive white Gaussian noise. Furthermore, experiments confirm the robustness of the proposed method against the range of combined attacks.

An improved block based copy-move forgery detection technique

With the increase in demand for identification of authenticity of the digital images, researchers are widely studying the image forgery detection techniques. Copy-move forgery is amongst the commonly used forgery, which is performed by copying a part of an image and then pasting it on the same or different image. This results in the concealing of image content. Most of the existing copy-move forgery detection techniques are subjected to degradation in results, under the effect of geometric transformations. In this paper, a Discrete Cosine Transformation (DCT) and Singular Value Decomposition (SVD) based technique is proposed to detect the copy-move image forgery. DCT is used to transform the image from the spatial domain to the frequency domain and SVD is used to reduce the feature vector dimension. Combination of DCT and SVD makes the proposed scheme robust against compression, geometric transformations, and noise. For classification of images as forged or authentic, Support Vector Machine (SVM) classifier is used on the feature set. Once the image is detected as forged, then for the localization of forged region, K-means clustering is used on the feature vector. According to the distance threshold, similar blocks are identified and marked. The application of SVD provides stability and invariance from geometric transformations. Evaluation of the proposed scheme is done with and without post-processing operations on the images, both at the pixel level and image level. The proposed scheme outperforms the various state-of-the-art techniques of Copy-Move Forgery Detection (CMFD) in terms of accuracy, precision, recall and F1 parameters. Moreover, the proposed scheme also provides better results against rotation, scaling, noise and JPEG compression.

A hybrid copy-move image forgery detection technique based on Fourier-Mellin and scale invariant feature transforms

In digital images, the most common forgery is copy-move image forgery in which some region(s) of an image is replicated within the image. The copy-move forgery detection (CMFD) techniques fall under two categories; keypoint-based and block-based. The keypoint-based techniques perform well under rotation and scaling but show very poor performance in the case of smooth images. On the contrary, the block-based techniques perform better in smooth images but are comparatively more time demanding. In this paper, a hybrid technique has been proposed by combining the block-based technique using Fourier-Mellin Transform (FMT) and a keypoint-based technique using Scale Invariant Feature Transform (SIFT). In this technique, the input image to be checked for forgery is first divided into texture and smooth regions. Then the keypoints are extracted from the texture part of the image using the SIFT descriptor, and the FMT is applied on the smooth part of the image. Extracted features are then matched to detect the duplicated regions of the image. The experimental results illustrate that the proposed technique performs better in comparison to other state-of-the-art CMFD techniques under various geometric transformations and post-processing operations in reasonable time.

S-transform-based efficient copy-move forgery detection technique in digital images

S-transform-based efficient copy-move forgery detection technique in digital images

S-transform-based efficient copy-move forgery detection technique in digital images

S-transform-based efficient copy-move forgery detection technique in digital images