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Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and tampered regions by analyzing intrinsic statistics in an image. We focus on JPEG compression artifacts left during image acquisition and editing. We propose a convolutional neural network that uses discrete cosine transform (DCT) coefficients, where compression artifacts remain, to localize image manipulation. Standard CNNs cannot learn the distribution of DCT coefficients because the convolution throws away the spatial coordinates, which are essential for DCT coefficients. We illustrate how to design and train a neural network that can learn the distribution of DCT coefficients. Furthermore, we introduce Compression Artifact Tracing Network that jointly uses image acquisition artifacts and compression artifacts. It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.

In the arena of image forensics, detecting manipulations in an image is extremely significant because of the use of images in different fields. Various detection techniques have been suggested in the literature that are based on digging out the features from images to unveil the traces left by manipulation operations. In this paper, a deep learning-based approach is proposed in which a residual network is used to learn deep, complex features from preprocessed images for classification into authentic and forged images. There is statistical symmetry in similar types of images and asymmetry in different types of images. The proposed scheme can highlight the statistical asymmetry between authentic and forged images. In the proposed scheme, firstly, an RGB image is analyzed for different JPEG compression levels. The obtained difference between the error levels is used to extract enhanced LBP code. Then, the scale- and direction-invariant LBP (SD-LBP) code is transformed into SD-LBP feature maps to feed to a deep residual network. Next, the concept of explainable artificial intelligence (XAI) is used to help provide explanations and interpret the output, thereby raising the credibility of the proposed approach. The unique feature selection approach employed is the kernel SHAP method, which is focused on the Shapley values. This technique is used to pinpoint the specific characteristics that are responsible for the aberrant behavior of the forged images dataset. Later, the deep learning-based model is trained and validated using these feature sets. A pre-activation version of ResNet-50 architecture is used that achieved an accuracy of 99.31%, 99.52%, 98.05%, and 99.10% on CASIA v1, CASIA v2, IMD 2020, and DVMM datasets, respectively. The capability of the pretrained residual network and rich textural features, which are scale- and direction-invariant, helps to expand the detection accuracy of the proposed approach. The results confirmed that the method either produced competitive results or outperformed existing methods.

With the development of image manipulation techniques and the widespread use of image editing tools, it is effortless to forge images without leaving an apparent manual trail. Existing methods achieve manipulation localization by detecting the anomalies in images. However, we argue that these methods mainly focus on tampering traces, and the learned features are representations of specific types of tampering in the training dataset, which are less effective in detecting tampering across diverse datasets. In this paper, we propose a novel Network using image Reconstruction and Reconstruction Attention (RRA-Net) that incorporates the anomaly detection idea for image tampering localization. By reconstructing the pristine part of images, we can learn the normal latent representation and mine the essential dissimilarities between the real and tampered regions. Furthermore, we use the reconstructed attention to guide the model in characterizing the tampered regions, thus facilitating manipulation localization. We conducted a series of experiments, and the results demonstrate that our RRA-Net outperforms the State-of-the-art (SOTA) methods and achieves good robustness against different post-processing attacks.

Misusing image tampering software makes it easier to manipulate satellite images, leading to a crisis of trust and security concerns in society. This study compares the inconsistencies between heterogeneous images to locate tampered areas and proposes a high-precision heterogeneous satellite image manipulation localization (HSIML) framework to distinguish tampered from real landcover changes, such as artificial constructions, and pseudo-changes, such as seasonal variations. The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. The semantic similarity measurement module designs a classification network to assess RS image feature saliency. It determines image consistency based on the similarity of semantic features and implements IML using predefined classification rules. Additionally, a dataset for IML is constructed based on satellite images. Extensive experiments compared with existing SOTA models demonstrate that our method achieved the highest F1 score in both localization accuracy and robustness tests and demonstrates the capability for handling large-scale areas.

Current research on image tampering localization focuses on finding region features that distinguish manipulated pixels from non-manipulated pixels. As tampering with a specific area of a given image inevitably leaves cues in the boundary between the tampered region and its surroundings, how to utilize sufficient region and boundary features also matters for image manipulation localization. In this paper, we propose a unified network (called RB-Net), which is a two-branch network (i.e., region module and boundary module) to learn region and boundary features separately. Then the fusion module is implemented to integrate the region features from the region module and the edge features from the boundary module, respectively. Particularly, to identify unnatural boundary traces, we propose edge gate components deployed on different layers of the region module to activate manipulated boundary information from the rich region features. Quantitative and qualitative experiments on four benchmark datasets demonstrate that RB-Net can accurately locate the tampered regions and achieve the best results relative to other state-of-the-art methods.

In recent times, numerous digital image manipulation detection approaches have been proposed to detect which processing operations were applied to manipulate digital images. Most of these approaches consider the situation in which an image is manipulated by only one manipulation operation. However, practical image manipulation often involves multiple manipulation operations. It is important to detect multiple image manipulation operations and the order in which they were applied to establish the origin and genuineness of a given image as well as the processing history it has gone through. In this article, we proposed a new method to determine multiple image processing operation and operation chains based on convolutional neural network (CNN) and local optimal oriented pattern (LOOP). The proposed method is based on CNN and LOOP in which CNN extracts and learns image manipulation traces from the LOOP maps of the input images that are classified using softmax, extra-tree, and extreme gradient boosting (XGBOOST) classifiers. Detailed experiments show that the proposed model can attain overall detection accuracies of 99.81% and 99.15% in identifying different image manipulations and manipulation operation chains, respectively.

Mining the forged regions of digitally tampered images is one of the key research tasks for visual recognition. Although there are many algorithms investigating image manipulation localization, most approaches focus only on the semantic information of the spatial domain and ignore the frequency inconsistency between authentic and tampered regions. In addition, the generality and robustness of the models are severely affected by the different noise distributions of the training and test sets. To address these issues, we propose the frequency-perception network with adversarial training for image manipulation localization. Our method not only captures representation information for boundary artifact identification in the spatial domain but also separates low and high-frequency information in the frequency domain to acquire tampered cues. Specifically, the frequency separation sensing module enriches the local sensing range by separating multi-scale frequency domain features. It accurately identifies high-frequency noise features in the manipulated region and distinguishes low-frequency information. The global frequency attention module uses multiple sampling and convolution operations to interactively learn multi-scale feature information and integrate dual-domain frequency content to identify tampered physical locations. Adversarial training is employed to construct hard training adversarial samples based on adversarial attacks to avoid interference from unevenly distributed redundant noise information. Extensive experimental results show that our proposed method performs significantly better than the mainstream approach on five common standard datasets.

In the field of image forensics, image tampering detection is a critical and challenging task. Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation, which limits their effectiveness in complex scenarios involving multiple forms of tampering. Although deep learning-based methods offer the advantage of automatic feature learning, current approaches still require further improvements in terms of detection accuracy and computational efficiency. To address these challenges, this study applies the U-Net 3+ model to image tampering detection and proposes a hybrid framework, referred to as DDT-Net (Deep Detail Tracking Network), which integrates deep learning with traditional detection techniques. In contrast to traditional additive methods, this approach innovatively applies a multiplicative fusion technique during downsampling, effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise stream. This design enables noise residual features to guide the learning of semantic features more precisely and efficiently, thus facilitating comprehensive feature-level interaction. Furthermore, by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’ proficiency in detecting fine-grained local traces, the method significantly enhances the accuracy and robustness of tampered region detection. Compared with other approaches, the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k datasets. In addition, it has been extensively validated on other datasets, including CASIA and DIS25k. Experimental results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

In this digital era, images are the major information carriers of contemporary society. Several multimedia manipulation tools like CorelDRAW, GIMP, Freehand, Adobe Photoshop, etc. are being used to forge the visual media for malicious reasons. It is becoming increasingly difficult to distinguish forged images from pristine images as a result of new manipulation techniques that have emerged over the past time. The most intriguing area of multimedia forensics research is image forgery detection. In the field of forensic image analysis, the most important task is to verify the authenticity of digital media. A novel passive approach for detecting digital image forgery is proffered in this manuscript. It is a sequential framework that uses a deep convolutional neural network to differentiate between original and altered images. On the COVERAGE dataset, numerous experiments have been evaluated in order to construct an effective and robust model, achieveing AUC value of 0.85 and F-measure of 0.70. The comparative results have been represented in summarized form and the results perform better than the state-of-the-art techniques.

With the recent advancement in modern technology, one can easily manipulate a digital image or video using computer software or a mobile application. The purpose of editing visual media could be as simple as to look good before sharing to the social networking site’s or can be as malicious as to defame or hurt one’s reputation in the real world through such morphed visual imagery. Identity theft is one of the examples where one’s identity get stolen by some impersonator who can access the personal and financial information of an innocent person. To avoid such drastic situations, law enforcement authorities must use some automatic tools and techniques to find out whether a person is innocent or the culprit. One major question that arises here is how and what parts of visual imagery can be manipulated or edited. The answer to this question is important to distinguish the authentic images/videos from the doctored multimedia. This survey provides a detailed analysis of image and video manipulation types, popular visual imagery manipulation methods, and state-of-the-art image and video forgery detection techniques. It also surveys different fake image and video datasets used in tampering. The goal is to develop a sense of privacy and security in the research community. Finally, it focuses to motivate researchers to develop generalized methods to capture artificial visual imagery which is capable of detecting any type of manipulation in given visual imagery.