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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.

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.

The swift growth of diverse editing software has resulted in image splicing manipulation becoming more complex, the discovery of a meticulously crafted splice forgery in digital images poses a significant challenge for both humans and machines. Existing image splicing manipulation detection algorithms have low localization accuracy and poor detection of small manipulation areas. In this paper, we proposed an end-to-end effective image manipulation location method based on a multi-scale and dual-channel model, MD_Unet. First, a dual-channel encoding network model is constructed. Adding a high-pass filtering branch containing SRM filters and Gabor filters at the input of the model and helps it to learn the manipulation traces of the image. Secondly, the dual-channel features are fused using an improved multi-scale pyramid pooling module. Then, Squeeze-Excitation is introduced to recalibrate the fused features so that the network pays more attention to splicing manipulation-related features. Finally, the fused feature map is input to the decoder, and the predicted image is decoded layer by layer to segment the manipulation region. We have performed extensive experimental validation and powerfully demonstrate the efficacy of the proposed approach.

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.

In the era of deepfakes and AI-generated content, digital image manipulation poses significant challenges to image authenticity, creating doubts about the credibility of images. Traditional image forensics techniques often struggle to detect sophisticated tampering, and passive detection approaches are reactive, verifying authenticity only after counterfeiting occurs. In this paper, we propose a novel full-resolution secure learned image codec (SLIC) designed to proactively prevent image manipulation by creating self-destructive artifacts upon re-compression. Once a sensitive image is encoded using SLIC, any subsequent re-compression or editing attempts will result in visually severe distortions, making the image’s tampering immediately evident. Because the content of an SLIC image is either original or visually damaged after tampering, images encoded with this secure codec hold greater credibility. SLIC leverages adversarial training to fine-tune a learned image codec that introduces out-of-distribution perturbations, ensuring that the first compressed image retains high quality while subsequent re-compressions degrade drastically. We analyze and compare the adversarial effects of various perceptual quality metrics combined with different learned codecs. Our experiments demonstrate that SLIC holds significant promise as a proactive defense strategy against image manipulation, offering a new approach to enhancing image credibility and authenticity in a media landscape increasingly dominated by AI-driven forgeries.