Digital Image Forgery Detection Research Articles

Squirrel Henry Gas Solubility Optimization driven Deep Maxout Network with multi‐texture feature descriptors for digital image forgery detection

SummaryThe ability to detect image forgeries is a crucial component in solving many problems, especially social ones that arise in legal proceedings, on Facebook, and so forth. Generally, forgery detection attempts to predict the artifacts through evaluating the differences in texture properties of an image. One division of an image is copied in a comparable image, typically at a different position, in a process known as copy move forgery. The two main types of copy move forgery prediction are keypoint‐based and block‐based techniques. While the block‐based approach takes more time, the keypoint‐based model performs poorly. The goal of this research was to develop a method for detecting image counterfeiting called the Squirrel Henry Gas Solubility Optimization algorithm‐based Deep Maxout Network (SHGSO‐based DMN). Here, Steerable Pyramid Transform (SPT) decomposition is done in order to obtain quantity of multi‐scale and multi‐oriented sub bands. For the purpose of detecting forgeries, the Deep Maxout Network which was constructed using the SHGSO model is deployed. With accuracy of 0.9507, True Negative Rate (TNR) of 0.9568, and True Positive Rate (TPR) of 0.9517, the new method outperformed other current forgery detection techniques.

Performance Evaluation of Local Binary Patterns LBP for Copy-Move Forgery Detection in Digital Images: A Comparative Study

Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

Comparison of specific segmentation methods used for copy move detection

<p><span lang="EN-US">In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature.</span></p>

A Thorough Investigation on Image Forgery Detection

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness. These methods have identified forgery and its type and compared it with state of the art. This work will help us to find the best forgery detection technique based on the different environments. It also shows the current issues in other methods, which can help researchers find future scope for further research in this field.

A deep learning framework for copy-move forgery detection in digital images

A deep learning framework for copy-move forgery detection in digital images

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features.

An improved approach for single and multiple copy-move forgery detection and localization in digital images

An improved approach for single and multiple copy-move forgery detection and localization in digital images

Digital image forgery detection under complex lighting using Phong reflection model

Image manipulation is transformed into a big issue for data integrity. The use of advanced imaging technology expends the regularity of multimedia forgeries. To detect such forgeries, some effective forgery identification methods are proposed to estimate the 3D lighting fingerprints by making certain suppositions related to the surface and reflection model. Incident lighting dispersal in a scene provides a physics-based key for exposing image exploitations. The proposed technique is more relaxed for multiple light source-based forgery detection. Also, the novelty of the technique is that it considers heterogeneous image surfaces, nearby surface geometry, and texture information to assess the lighting environment. The Phong reflection model is implemented to estimate the structural profiles. The method works in two phases. In the first phase, pre-processing over selected patches is performed followed by angle and error estimations in the second phase. To identify such forgeries, elevation angles ϑ concerning mounted light sources are estimated. The value of the elevation angle is computed for various patches of the image. The proposed method is relevant for a wide range of objects present in the image, i.e., not only limited to head positions, etc. To build up and validate the technique, comprehensive testing on the synthetic image dataset is done. Synthetic images are designed under different intensity light sources. Experimental results demonstrate a better forgery detection accuracy compared with other state-of-the-art methods in this domain. Also, the proposed technique is tested for generalized forged images. A comparative analysis with other related techniques is done to validate the proposed method using a synthetic image dataset. The results are pertinent to all type of images, and an error threshold is limited to 8 degrees with respect to the illuminant position.

Forgery Detection in Digital Images by Multi-Scale Noise Estimation

A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.

Analysis of Keypoint-based Copy-Move Forgery Detection in Digital Images

The images are used as the element that is primary of in multitudinous operations and the dupe- move phony is considerably used to induce the forged images by manipulating the content of digital images. With the advancement of various image editing software Photoshop that is including Draw, GIMP, etc make it's easier to manipulate the digital images. Thus, Copy- Move Forgery Detection is a task that is challenging. Some of the dupe- move phony discovery styles analogous as Symmetry predicated features that are original SIFT- Symmetry, Clustering with J- relation Algorithm, SIFT- KAZE crossbred Features, point Matching predicated on frippery- bin-First Algorithm, Binary Discriminant Featuresetc. are compared and analysed in this paper.

Fusion of Handcrafted and Deep Features for Forgery Detection in Digital Images

Content authentication of digital images has captured the attention of forensic experts and security researchers due to a multi-fold increase in the dissemination of multimedia data through the open and vulnerable Internet. Shrewd attackers successfully devise novel ways to challenge state-of-art forensic tools used for forgery detection in digital images. Feature engineering approaches have yielded up to 97% accuracy on benchmarked datasets. Deep learning approaches have shown promising results in various image classification problems but cannot find hidden patterns in digital images, which can reliably detect image forgeries. State-of-art accuracy of deep learning approaches for forgery detection is up to 98% on benchmarked datasets. The objective of the proposed approach is to further escalate the detection accuracy, pushing it near 100%. In this paper, a synergy of handcrafted features based on color characteristics and deep features using the image’s luminance channel is employed to mine patterns responsible for accurate forgery detection. In the first Stream, 648-D Markov-based features are computed from the quaternion discrete cosine transform of the image. In the second Stream, the luminance channel of YCbCr colorspace is used to extract the Local Binary Pattern of the image. Further, local binary feature maps are fed to the pre-trained ResNet-18 model to obtain a 512-D feature vector called ’ResFeats’ from the last layer of the model’s convolutional base portion. The handcrafted features from Stream I and ResFeats from Stream II are combined to form an 1160-D feature vector. Further, classification is performed using a shallow neural network, and the method is tested on CASIA v1 and CASIA v2 datasets. The accuracy of the proposed fusion-based approach is 99.3% on benchmark datasets.

Development of Digital Image Forgery Detection Method Utilizing LE(Local Effect) Operator based on L0 Norm

Development of Digital Image Forgery Detection Method Utilizing LE(Local Effect) Operator based on L0 Norm

Digital Image Forgery Detection Approaches: A Review and Analysis

Digital images have an inevitable role in almost all areas like clinical imaging, media broadcasting, crime analysis and scientific analysis etc. The phrase, “One image is equal to a thousand words”, is exactly fit as an image has more expressive capability than text messages. In all investigation, a photograph was always perceived as ‘proof of occurrence of an event’, due to a strong observation that, ‘seeing is believing ‘. Hence, images were considered as a ‘piece of truth’. Normally, an image is authentic if it is an originally recorded or captured from an actual scene or situation using any image capturing device. The captured image is expected to convey the original situation or scene at the source while capturing it in a real sense. In recent decades, due to the accessibility of modern photograph content altering software tools, it is very easy to transform the content of the image and therefore authenticity and integrity of the image is meager and tampering in digital images therefore does not require any specialist skills. This paper presents the modern methodological assessment and analysis of recent image forgery detection techniques. The various methods used in each stage of forgery detection techniques are also briefed. For an immediate reference, the tables of comparison are provided. The subject review paper is intended to help researchers to deliver useful understandings and modernized info about ongoing progress in forgery detection.

An efficient approach for forgery detection in digital images using Hilbert–Huang​ transform

Image manipulation plays important role in fake news spreading and it may cause ethical, economic, or political problems for people and sometimes for countries. Image integrity verification becomes a very important research issue due to increasing the forged images on the Internet and social media. The objective of this paper is presenting an accurate approach for digital image forgery detection has enough capability to sense any small image tampering and robustness against image manipulation attacks. The first step in the proposed approach is converting the RGB image into YCbCr space, then, the Hilbert–Huang Transform (HHT) features extracted from the chrominance-red component Cr, then, three different classifiers; Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Artificial Neuron Networks (ANN) have been tested and compared for image classification into authentic or forged. The results are verified using Structural-Similarity (SSIM) to calculate the forgery detection accuracy. The proposed approach has been tested with seven different manipulation images datasets; CASIA-V1, CASIA-V2, MICC-F2000, MICC-F600, MICC-F220, CoMoFoD and additional dataset collected from different Internet websites and social media. Furthermore, the proposed approach has been tested against post-processing attacks such as; image compression, adding Gaussian noises or adjusting the contrast of the image. The results show that, SVM classifier has achieved the highest accuracy compared to ANN and KNN classifiers. The proposed approach has been compared with other published approaches, and the comparison proved its superiority over the previously published approaches.

Single and Multiple Copy–Move Forgery Detection and Localization in Digital Images Based on the Sparsely Encoded Distinctive Features and DBSCAN Clustering

Due to the advancements in digital image processing and multimedia devices, the digital image can be easily tampered and presented as evidence in judicial courts, print media, social media, and for insurance claims. The most commonly used image tampering technique is the copy-move forgery (CMF) technique, where the region from the original image is copied and pasted in some other part of the same image to manipulate the original image content. The CMFD techniques may not provide robust performance after various post-processing attacks and multiple forged regions within the images. This article introduces a robust CMF detection technique to mitigate the aforementioned problems. The proposed CMF detection technique utilizes a fusion of speeded up robust features (SURF) and binary robust invariant scalable keypoints (BRISK) descriptors for CMF detection. The SURF features are robust against different post-processing attacks such as rotation, blurring, and additive noise. However, the BRISK features are considered as robust in the detection of the scale-invariant forged regions as well as poorly localized keypoints of the objects within the forged image. The fused features are matched using hamming distance and second nearest neighbor. The matched features grouped into clusters by applying density-based spatial clustering of applications with noise clustering algorithm. The random sample consensus technique is applied to the clusters to remove the remaining false matches. After some post-processing, the forged regions are detected and localized. The performance of the proposed CMFD technique is assessed using three standard datasets (i.e., CoMoFoD, MICC-F220, and MICC-F2000). The proposed technique surpasses the state-of-the-art techniques used for CMF detection in terms of true and false detection rates.

Digital image forgery detection using deep learning approach

This paper presents an algorithm for detecting one of the most commonly used types of digital image forgeries - splicing. The algorithm is based on the use of the VGG-16 convolutional neural network. The proposed network architecture takes image patches as input and obtains classification results for a patch: original or forgery. On the training stage we select patches from original image regions and on the borders of embedded splicing. The obtained results demonstrate high classification accuracy (97.8% accuracy for fine-tuned model and 96.4% accuracy for the zero-stage trained) for a set of images containing artificial distortions in comparison with existing solutions. Experimental research was conducted using CASIA dataset.

Image Forgery Detection and Localization using Modified JPEG Ghost

Authenticity of an image taken digitally suffers severe threats as a result of increase in various powerful digital image editing tools. These tools modifies the image contents without leaving footprint of such modifications. We come up with a technique that analyzes digital image forgery detection in JPEG images which goes through multiple compression. Nearly all digital devices uses JPEG as a standard storage format to maintain the storage space. JPEG is a lossy compression standard. By using any image processing tools, when assailant changes any part of a JPEG image and save it, the alter part of the image has different compression artifacts. JPEG ghost algorithm is used to detect disparity in JPEG blocks that rise from improper alignments of JPEG blocks respect to original structure and detect local footprint of JPEG compression. In our work, our proposed technique will modify JPEG ghost detection to detect and localize digital image forgery.

A robust and forensic transform for copy move digital image forgery detection based on dense depth block matching

ABSTRACTCopy-move forgery is one of the most popular tampering artefacts in digital images. However, tampering effect in digital images makes the authentication of the processing as untrustworthy. In this paper, a combination of Fourier-Mellin and Zernike moments (FMZM) Transform is proposed which detects the copy-move region with high-speed and low-computational complexity. Here, initially an image is segmented into various blocks using marker controlled watershed management and from that proposed FMZM feature extraction is used which detects duplication. The detected regions are matched with the Dense Depth Reconstruction based lexicographically sorting. Finally, tampered outliers presented at the data are removed through RANSAC (RANdom Sample Consensus) algorithm, in which removed false matches are verified with the morphological operators. The efficiency of proposed method is measured by various performance metrics and this method earned up to 97.56%, 99.98%, and 97.12% for precision, recall, and F1-score performance, respectively.

New and efficient blind detection algorithm for digital image forgery using homomorphic image processing

Digital image forgery detection is an important task in digital life as the image may be easily manipulated. This paper presents a novel blind tampering detection algorithm for images acquired from digital cameras and scanners. The algorithm is based on applying homomorphic image processing on each suspicious image to separate illumination from reflectance components. In natural images, it is known that the illumination component is approximately constant, while changes can be detected in tampered ones. Support Vector Machine (SVM) and Neural Network (NN) classifiers are used for classification of tampered images based on the illumination component, and their results are compared to obtain the best classifier performance. The Receiver Operating Characteristic (ROC) curve is used to depict the classifier performance. Three different color coordinate systems are tested with the proposed algorithm, and their results are compared to obtain the highest accuracy level. Joint Photographic Experts Group (JPEG) compressed images with different Quality Factors (QFs) are also tested with the proposed algorithm, and the performance of the proposed algorithm in the presence of noise is studied. The performance of the SVM classifier is better than that of the NN classifier as it is more accurate and faster. A 96.93% detection accuracy has been obtained regardless of the acquisition device.

Region duplication detection in digital images based on Centroid Linkage Clustering of key–points and graph similarity matching

Region duplication or copy–move forgery is an attack in which a region of an image is copied and pasted onto another location of the same image. In the recent state–of–the–art, a number of key–point based methods have been proposed for copy–move forgery detection in digital images. Though the problems of re–scaling and rotation in region duplication, have been sufficiently investigated using key–point based methods, post-processing based attacks such as flip, blur, brightness and noise, remain an open challenge in this field. In this paper, we address the problem of copy–move forgery detection in images, plus aim to identify copied regions, having undergone different geometric (such as rotation, re–scale), and post–processing attacks (such as Gaussian noise, blurring and brightness adjustment). In the proposed algorithm we introduce a region based key–point selection concept, which is considerably more discriminative than single SIFT key–point extraction. In this work, we apply Centroid Linkage Clustering, to identify duplicated regions in an image, from matched key-points. Also, we introduce a Graph Similarity Matching algorithm, to optimize false matches. Our experimental results demonstrate the efficiency of the proposed method in terms of forgery detection and localization efficiency, for a wide range of geometric and post-processing based attacks in region duplication.