Localization of Image Splicing Under Segment Anything Model With Integrated Compression and Edge Artifacts

Detecting and localizing image splicing has become essential to fight against malicious forgery. A major challenge to localize spliced areas is to discriminate between authentic and tampered regions with intrinsic properties such as compression artifacts. We propose CAT-Net, an end-to-end fully convolutional neural network including RGB and DCT streams, to learn forensic features of compression artifacts on RGB and DCT domains jointly. Each stream considers multiple resolutions to deal with spliced object's various shapes and sizes. The DCT stream is pretrained on double JPEG detection to utilize JPEG artifacts. The proposed method outperforms state-of-the-art neural networks for localizing spliced regions in JPEG or non-JPEG images.

Compression artifacts are the results of an aggressive data compression scheme applied to an image that discards some data which is determined by an algorithm to be of lesser importance to the overall content but which is nonetheless discernible and objectionable to the user. In this paper we present a progressive algorithm that focus on preserving the clarity of important image features, such as edges, and reducing the compression artifacts around edges at compression ratios of 100:1 and greater. The algorithm extracts the locations of straight line edges in the image at the encoder, and the decoder applies edge extraction, combination, artifacts reduction and linear edge-enhancement procedure to improve the clarity of the edges. With this algorithm, edges in the images that may be important for recognition are well preserved, and compression artifacts are reduced even at very low bit-rates

Today, society is completely dependent on the utilization of Internet. With the increase in use of social media, millions of pictures are daily uploaded to Internet, providing opportunities for hackers to forge images. Various image editing softwares have opened the ways to image forgery, making forged images to look authentic. The manipulations of content have dissolved image trustworthiness and validation. Advancement in image forensics has introduced a number of image forgery detection techniques, to reestablish the realness in digital media. This paper endeavors to reveal various kinds of image forgery and its recognition techniques. The paper has also presented the performance of the existing splicing techniques by using quality metrics MCC and F-measure. Further, the paper evaluates different state of the art in splicing techniques, and it has been observed that the CFA artifact-based splicing localization achieves an accuracy of 99.75%. This paper features the significance of splicing localization and possible future research work in it.

In a tampered blurred image generated by splicing, the spliced region and the original image may have different blur types. Splicing localization in this image is a challenging problem when a forger uses some postprocessing operations as antiforensics to remove the splicing traces anomalies by resizing the tampered image or blurring the spliced region boundary. Such operations remove the artifacts that make detection of splicing difficult. In this paper, we overcome this problem by proposing a novel framework for blurred image splicing localization based on the partial blur type inconsistency. In this framework, after the block-based image partitioning, a local blur type detection feature is extracted from the estimated local blur kernels. The image blocks are classified into out-of-focus or motion blur based on this feature to generate invariant blur type regions. Finally, a fine splicing localization is applied to increase the precision of regions boundary. We can use the blur type differences of the regions to trace the inconsistency for the splicing localization. Our experimental results show the efficiency of the proposed method in the detection and the classification of the out-of-focus and motion blur types. For splicing localization, the result demonstrates that our method works well in detecting the inconsistency in the partial blur types of the tampered images. However, our method can be applied to blurred images only.

Image splicing can be easily used for illegal activities such as falsifying propaganda for political purposes and reporting false news, which may result in negative impacts on society. Hence, it is highly required to detect spliced images and localize the spliced regions. In this work, we propose a multi-task squeeze and excitation network (SE-Network) for splicing localization. The proposed network consists of two streams, namely label mask stream and edge-guided stream, both of which adopt convolutional encoder-decoder architecture. The information from the edge-guided stream is transmitted to the label mask stream for enhancing the discrimination of features between the spliced and host regions. This work has three main contributions. First, image edges, along with label masks and mask edges, are exploited to supply more comprehensive supervision for the localization of spliced regions. Second, the low-level feature maps extracted from shallow layers are fused with the high-level feature maps from deep layers to provide more reliable feature for splicing localization. Finally, several squeeze and excitation attention modules are incorporated into the network to recalibrate the fused features to enhance the feature expression. Extensive experiments show that the proposed multi-task SE-Network outperforms existing splicing localization methods evidently on two synthetic splicing datasets and four benchmark splicing datasets.

In a spliced blurred image, the spliced region and the original image may have different blur types. Splicing localization in this image is challenging when a forger uses image resizing as anti-forensics to remove the splicing traces anomalies. In this paper, we overcome this problem by proposing a method for splicing localization based on partial blur type inconsistency. In this method, after the block-based image partitioning, a local blur type detection feature is extracted from the estimated local blur kernels. The image blocks are classified into out-of-focus or motion blur based on this feature to generate invariant blur type regions. Finally a fine splicing localization is applied to increase the precision of regions boundary. We can use the blur type differences of the regions to trace the inconsistency for the splicing localization. Our experimental results show the efficiency of the proposed method in the detection and the classification of the out-of-focus and motion blur types.

The tremendous technological advancements in recent times has enabled people to create, edit and circulate images easily than ever before. As a result of this, ensuring the integrity and authenticity of the images has become challenging. Malicious editing of images to deceive the viewer is referred to as image tampering. A widely used image tampering technique is image splicing or compositing, in which regions from different images are copied and pasted. In this paper, we propose a tamper detection method utilizing the blocking and blur artifacts which are the footprints of splicing. The classification of images as tampered or not, is done based on the standard deviations of the entropy histograms and block discrete cosine transformations. We can detect the exact boundaries of the tampered area in the image, if the image is classified as tampered. Experimental results on publicly available image tampering datasets show that the proposed method outperforms the existing methods in terms of accuracy.

In image splicing tamper detection, forgery operations simultaneously introduce macroscopic semantic inconsistencies and microscopic tampering artifacts. Conventional methods often treat semantic understanding and low-level artifact perception as separate tasks, which impedes their effective synergy. Meanwhile, frequency-domain information, a crucial clue for identifying traces of tampering, is frequently overlooked. However, a simplistic fusion of frequency-domain and spatial features can lead to feature conflicts and information redundancy. To resolve these challenges, this paper proposes an Adaptive Multi-Scale Edge-Aware Network (AMSEANet). This network employs a synergistic enhancement cascade architecture, recasting semantic understanding and artifact perception as a single, frequency-aware process guided by deep semantics. It leverages data-driven adaptive filters to precisely isolate and focus on edge artifacts that signify tampering. Concurrently, the dense fusion and enhancement of cross-scale features effectively preserve minute tampering clues and boundary details. Extensive experiments demonstrate that our proposed method achieves superior performance on several public datasets and exhibits excellent robustness against common attacks, such as noise and JPEG compression.

With the advances in digital media, images have been used as one of the most significant sources of information and communication in the modern society. However, images can be manipulated easily with the widespread availability of advanced image editing tools. Image splicing is one of the most popular image forgery methods in which a portion of one image is copied and stitched into another image for misleading the information. Hence, detection of image splicing forgery has become a significant research problem in multimedia forensic. Many splicing detection algorithms have been developed using traditional hand-crafted features, but these algorithms could not perform well on many post-processed or geometrically transformed spliced images. Hence in this paper, a new encoder-decoder architecture using convolutional neural network (CNN) is designed to locate the spliced region in an image. A hybrid CNN model called ResUNet is proposed in which encoder part of standard UNet architecture is replaced by ResNet 50 to analyze the discriminating feature between tampered and untampered regions. The proposed model is evaluated on CASIA v2.0 a benchmark dataset and experimental results demonstrate the proposed model turned out to be more efficient and accurate in splicing localization than other existing techniques.

Most current neural network-based splicing localization methods are based on subtle telltales from inter-pixel differences. But for recompressed and downsampled data, these artifacts are weakened. In this paper, we propose a novel multi-level feature enhancement network (MFENet) to enhance the features. Tampering with an image not only destroys the consistency of the inherent high-frequency noise in host images, but also is performed post-processing operations to weaken this discrepancy. Therefore, based on the high-pass filtered image residuals, we combine the detection evidence of post-processing operations to complete splicing forensic task. For the purpose of enhancing the distinguishability of features in the residual domain, we use bilinear pooling to fuse low-level manipulation features and residuals. In order to improve the consistency between the ground truth and the splicing localization result, we integrate global attention modules to minimize the intra-class variance by measuring the similarity of features. Finally, we propose a multi-scale training generation strategy to train our network, which provides local and global information for the input and pays more attention to the overall localization during gradient feedback. The experimental results show that our method achieves better performance than other state-of-the-art methods.KeywordsSplicing localizationPost-processing operationsManipulation

A visual model which gives a distortion measure for Blocking Artifacts in images is presented. Given the original and the reproduced image as inputs, the model output is a single numerical value which quantifies the visibility of blocking error in the reproduced image. The model is derived based on the human visual sensitivity to horizontal and vertical edge artifacts which result from blocking. Psychovisual experiments have been carried out using a novel experimental technique to measure the sensitivity to edge artifacts with the variation of edge length, edge amplitude, background luminance and background activity. The model parameters are estimated based on these sensitivity measures. The final model has been tested on real images, and the results show that the error predicted by the model correlate well with the subjective ranking.

Digital images are the fastest means of transferring information in the present era, but with the ubiquitousness of advanced photo-editing tools, image forgery has become easier and more frequent. A common class of image forgery techniques is called “splicing”, in which, the forger crops a region of the first image and places it in the second image. Doing so, the difference in the blur type of different regions can leave a trace for tracking the forgery, that is, the blur type of different regions are going to be inconsistent if the source and spliced images are different in their class of blurriness. An approach to detect splicing in images is to evaluate the inconsistency of statistical features which results when splicing occurs. Considering the importance of splicing detection, various methods have been proposed. Nevertheless, the lack of public benchmark dataset for fairly evaluating the splicing detection methods is a major problem. Consequently, we were motivated to prepare a dataset for exploiting the inconsistency of blur types with the purpose of localizing splicing in forged images. We called the first version of our dataset SBU-TIDED1 (SBU Tampered Image Detection Evaluation Dataset). Furthermore, we have explored the features used in blur type detection. A new set of features is proposed which leads to accuracy enhancement. It is apparent form the experimental results that the proposed features are efficient for detecting and classifying two blur types, namely, out-of-focus and motion blur.

Image splicing is a forgery technique where some regions are cropped or pasted from the same or different images. Splicing localization becomes challenging when post-processing techniques are used to remove the anomalies of splicing traces. In this chapter, an improved method is proposed for blurred image splicing localization based on K-nearest neighbor (KNN) matting. The proposed method minimizes computation time without compromising the quality of the result. Quantitative and qualitative results analysis show the proposed method obtains better splicing than existing systems.

With the proliferation of smartphones and social media, journalistic practices are increasingly dependent on information and images contributed by local bystanders through Internet-based applications and platforms. Verifying the images produced by these sources is integral to forming accurate news reports, given that there is very little or no control over the type of user-contributed content, and hence, images found on the Web are always likely to be the result of image tampering. In particular, image splicing, i.e. the process of taking an area from one image and placing it in another is a typical such tampering practice, often used with the goal of misinforming or manipulating Internet users. Currently, the localization of splicing traces in images found on the Web is a challenging task. In this work, we present the first, to our knowledge, exhaustive evaluation of today's state-of-the-art algorithms for splicing localization, that is, algorithms attempting to detect which pixels in an image have been tampered with as the result of such a forgery. As our aim is the application of splicing localization on images found on the Web and social media environments, we evaluate a large number of algorithms aimed at this problem on datasets that match this use case, while also evaluating algorithm robustness in the face of image degradation due to JPEG recompressions. We then extend our evaluations to a large dataset we formed by collecting real-world forgeries that have circulated the Web during the past years. We review the performance of the implemented algorithms and attempt to draw broader conclusions with respect to the robustness of splicing localization algorithms for application in Web environments, their current weaknesses, and the future of the field. Finally, we openly share the framework and the corresponding algorithm implementations to allow for further evaluations and experimentation.

Image splicing forgery is one of the significant image manipulations. It is very difficult to find the location of forged portion in a spliced image. Nowadays, deep neural networks have been used to detect and localize image forgeries. In this paper, a brief survey and comparison on splicing localization of images using Fully Convolutional Network (FCN), a deep neural network is presented.