Splicing Localization Research Articles

Spatio-temporal co-attention fusion network for video splicing localization

Spatio-temporal co-attention fusion network for video splicing localization

UGEE-Net: Uncertainty-guided and edge-enhanced network for image splicing localization

Image splicing, a prevalent method for image tampering, has significantly undermined image authenticity. Existing methods for Image Splicing Localization (ISL) struggle with challenges like limited accuracy and subpar performance when dealing with imperceptible tampering and multiple tampered regions. We introduce an Uncertainty-Guided and Edge-Enhanced Network (UGEE-Net) for ISL to tackle these issues. UGEE-Net consists of two core tasks: uncertainty guidance and edge enhancement. We employ Bayesian learning to model uncertainty maps of tampered regions, directing the model's focus to challenging pixels. Simultaneously, we employ a frequency domain-auxiliary edge enhancement strategy to imbue localization features with global contour information and fine-grained local details. These mechanisms work in parallel, synergistically boosting performance. Additionally, we introduce a cross-level fusion and propagation mechanism that effectively utilizes contextual information for cross-layer feature integration and leverages channel-level correlations for cross-layer feature propagation, gradually enhancing the localization feature's details. Experiment results affirm UGEE-Net's superiority in terms of detection accuracy, robustness, and generalization capabilities. Furthermore, to meet the growing demand for high-quality datasets in image forensics, we present the HTSI12K dataset, which includes 12,000 spliced images with imperceptible tampering traces and diverse categories, rendering it suitable for real-world auxiliary model training.

Adversarial Learning for Constrained Image Splicing Detection and Localization

Abstract: In the era of digital media, the manipulation of images has become a significant concern, particularly on social media platforms. Adversarial Learning for Constrained Image Splicing Detection and Localization aims to develop a robust system capable of recognizing and localizing spliced or tampered images. This research proposes a novel approach that leverages Convolutional Neural Networks (CNNs) and adversarial learningtechniques to enhance the detection and localization of image splicing. The system is designed to aid in distinguishing between authentic and manipulated images, thereby promoting transparency and trust in digital media. With the rapid advancement of digital media and image editingtools, the manipulation of visual content has become increasingly prevalent, posing significant challenges to the credibility and trustworthiness of images shared on social media platforms. One pervasive form of image manipulation is image splicing, where portions of two or more images are seamlessly combined to create a composite image, often with the intent to mislead or deceive viewers. Adversarial Learning for Constrained Image Splicing Detection and Localization aims to address this issue by developing a robust system capable of accurately detecting and localizing spliced regions within tampered images. This research proposes a novel approach that synergistically integrates Convolutional Neural Networks (CNNs) and adversarial learning techniques to enhance the performance of image splicing detection and localization. The proposed system employs a two-stage process: first, a CNN-based classifier is trained on a large dataset of authentic and spliced images to learn discriminative features for distinguishing between the two classes; subsequently, an adversarial learning model is employed to generate adversarial examples that can deceive the CNN classifier, while the classifier is iteratively updated to become more robust against these adversarial perturbations. By accurately identifying and localizing spliced regions within images, this research contributes to promoting transparency and trust in digital media shared on social media platforms, ultimately empowering users to make informed decisions about the authenticity of visual content. The proposed system has potential applications in various domains, including journalism, law enforcement, and content moderation, where verifying the integrity of visual evidence is of paramount importance.

End-to-end image splicing localization based on multi-scale features and residual refinement module

End-to-end image splicing localization based on multi-scale features and residual refinement module

MSA-Net: Multi-scale attention network for image splicing localization

MSA-Net: Multi-scale attention network for image splicing localization

Color Image Splicing Localization Based on Block Classification Using Transition Probability Matrix

With the increasing technology, digital images have become a widely used data type in crucial areas such as medical journalism and law. Since it is used in such important areas, it has become questionable whether digital images are original or not. Image splicing forgery is one of the most common forgery types applied to digital images. This work proposes a new image splicing detection and localization method. Our motivation is to reveal the boundaries of forgery by using statistical features of the image blocks. The proposed method has two main stages: training and localizing. In both phases, image blocks that contain edge information are used because the splicing operation causes some inconsistency on the edges. In the training stage, original blocks are selected from the regions that include original boundaries, and forged blocks are selected from the areas that contain splicing operation-induced edges. Transition probability matrices are calculated in eight directions to obtain the correlation of the borders between the neighbor blocks on original and splicing edges. These matrices are used as a feature for each block. The blocks are classified as authentic and spliced using SVM. A new post-processing step has been proposed to eliminate the false positives that may occur due to the presence of original regions that are likely to be detected as spliced edges in the image. The publicly available Columbia dataset has been used to show the effectiveness of the state-of-the-art and proposed method. The results indicate that the proposed method has performed well even under JPEG compression and Gaussian blurring attacks.

Correction to: Detection and localization of splicing on remote sensing images using image-to-image transformation

Correction to: Detection and localization of splicing on remote sensing images using image-to-image transformation

Multi-scale attention context-aware network for detection and localization of image splicing

Multi-scale attention context-aware network for detection and localization of image splicing

Pattern recognition in the landscape of seemingly random chimeric transcripts

The molecular and functional diversity generated by chimeric transcripts (CTs) that are derived from two genes is indicated to contribute to tumor cell survival. Several gaps yet exist. The present research is a systematic study of the spectrum of CTs identified in RNA sequencing datasets of 160 ovarian cancer samples in the The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov). Structural annotation revealed complexities emerging from chromosomal localization of partner genes, differential splicing and inclusion of regulatory, untranslated regions. Identification of phenotype-specific associations further resolved a dynamically modulated mesenchymal signature during transformation. On an evolutionary background, protein-coding CTs were indicated to be highly conserved, while non-coding CTs may have evolved more recently. We also realized that the current premise postulating structural alterations or neighbouring gene readthrough generating CTs is not valid in instances wherein the parental genes are genomically distanced. In addressing this lacuna, we identified the essentiality of specific spatiotemporal arrangements mediated gene proximities in 3D space for the generation of CTs. All these features together suggest non-random mechanisms towards increasing the molecular diversity in a cell through chimera formation either in parallel or with cross-talks with the indigenous regulatory network.

Detection and localization of splicing on remote sensing images using image-to-image transformation

Detection and localization of splicing on remote sensing images using image-to-image transformation

Multi-Task SE-Network for Image Splicing Localization

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.

A Novel Deep Learning Architecture with Multi-Scale Guided Learning for Image Splicing Localization

The goal of image splicing localization is to detect the tampered area in an input image. Deep learning models have shown good performance in such a task, but are generally unable to detect the boundaries of the tampered area well. In this paper, we propose a novel deep learning model for image splicing localization that not only considers local image features, but also extracts global information of images by using a multi-scale guided learning strategy. In addition, the model integrates spatial and channel self-attention mechanisms to focus on extracting important features instead of restraining unimportant or noisy features. The proposed model is trained on the CASIA v2.0 dataset, and its performance is tested on the CASIA v1.0, Columbia Uncompressed, and DSO-1 datasets. Experimental results show that, with the help of the multi-scale guided learning strategy and self-attention mechanisms, the proposed model can locate the tampered area more effectively than the state-of-the-art models.

Live imaging of RNA and RNA splicing in mammalian cells via the dcas13a-SunTag-BiFC system

Dynamic tracking of the localization of RNA molecules (nucleus and/or cytoplasm) and RNA splicing in living cells plays an important role in understanding their functions. However, a lack of dynamic imaging and high background fluorescence have been reported in the fluorescence in situ hybridization (FISH). Here, we developed a new tool, the dcas13a-SunTag-BiFC system, which fused the dLwacas13a and SunTag systems. dLwacas13a is used as a tracker to target specific RNAs, while SunTag recruits split Venus fluorescent proteins to label targeted RNAs. Our results showed that 4 × NLS-dCas13a-24 × SunTag-BiFC and 2 × NLS- dCas13a-24 × SunTag-BiFC systems can be used for imaging of endogenous RNA foci in the nucleus (Xist) and cytoplasm (Ppib and stress granules) in living cells, respectively. Compared to 12x MS2-MCP system, the dcas13a-SunTag-BiFC system showed a better performance of mRNA foci tracking in live cells. Furthermore, we confirmed the premature termination codon (PTC)-induced exon skipping of Oxt RNA using the dcas13a-SunTag-BiFC and MS2-MCP systems in the nucleus. Thus, the dcas13a-SunTag-BiFC system will facilitate the study of RNA localization in living cells and provide new insights into RNA translocation and splicing.

NoiseSeg: An image splicing localization fusion CNN with noise extraction and error level analysis branches

To counter the ever increasing flood of image forgeries in the form of spliced images in social media and the web in general, we propose the novel image splicing localization CNN NoiseSeg. NoiseSeg fuses statistical and CNN-based splicing localization methods in separate branches to leverage the benefits of both. Unique splicing anomalies that can be identified by its coarse noise separation branch, fine-grained noise feature branch and error level analysis branch all get combined in a segmentation fusion head to predict a precise localization of the spliced regions. Experiments on the DSO-1, CASIAv2, DEFACTO, IMD2020 and WildWeb image splicing datasets show that NoiseSeg outperforms most other state-of-the-art methods significantly and even up to a margin of 46.8%.

Amplitude SAR Imagery Splicing Localization

Synthetic Aperture Radar (SAR) images are a valuable asset for a wide variety of tasks. In the last few years, many websites have been offering them for free in the form of easy to manage products, favoring their widespread diffusion and research work in the SAR field. The drawback of these opportunities is that such images might be exposed to forgeries and manipulations by malicious users, raising new concerns about their integrity and trustworthiness. Up to now, the multimedia forensics literature has proposed various techniques to localize manipulations in natural photographs, but the integrity assessment of SAR images was never investigated. This task poses new challenges, since SAR images are generated with a processing chain completely different from that of natural photographs. This implies that many forensics methods developed for natural images are not guaranteed to succeed. In this paper, we investigate the problem of amplitude SAR imagery splicing localization. Our goal is to localize regions of an amplitude SAR image that have been copied and pasted from another image, possibly undergoing some kind of editing in the process. To do so, we leverage a Convolutional Neural Network (CNN) to extract a fingerprint highlighting inconsistencies in the processing traces of the analyzed input. Then, we examine this fingerprint to produce a binary tampering mask indicating the pixel region under splicing attack. Results show that our proposed method, tailored to the nature of SAR signals, provides better performances than state-of-the-art forensic tools developed for natural images.

Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images

One common task in image forensics is to detect spliced images, where multiple source images are composed to one output image. Most of the currently best performing splicing detectors leverage high-frequency artifacts. However, after an image underwent strong compression, most of the high frequency artifacts are not available anymore. In this work, we explore an alternative approach to splicing detection, which is potentially better suited for images in-the-wild, subject to strong compression and downsampling. Our proposal is to model the color formation of an image. The color formation largely depends on variations at the scale of scene objects, and is hence much less dependent on high-frequency artifacts. We learn a deep metric space that is on one hand sensitive to illumination color and camera white-point estimation, but on the other hand insensitive to variations in object color. Large distances in the embedding space indicate that two image regions either stem from different scenes or different cameras. In our evaluation, we show that the proposed embedding space outperforms the state of the art on images that have been subject to strong compression and downsampling. We confirm in two further experiments the dual nature of the metric space, namely to both characterize the acquisition camera and the scene illuminant color. As such, this work resides at the intersection of physics-based and statistical forensics with benefits from both sides.

VEGF gene polymorphisms regulate human retinal vascular endothelial cell proliferation and apoptosis through ASF/SF2-associated alternative splicing

This study investigated the effects of single nucleotide polymorphisms (SNPs) of the VEGF （vascular endothelial growth factor） gene, which are associated with susceptibility to age-related macular degeneration (AMD), on the expression of VEGF proteins (VEGF165 and VEGF165b) and their role in cell proliferation and apoptosis in human retinal vascular endothelial cells (hRVECs). Cell viability and VEGF165 and VEGF165b expressions were evaluated in hRVECs transfected with VEGF genes containing different SNPs (rs3025039, rs3025033, and rs10434). The Cell Counting Kit 8 assay, quantitative real-time PCR, western blotting, TUNEL assay, and enzyme-linked immunosorbent assay were used to examine the effects of VEGF gene SNPs on cell viability, VEGF165 and VEGF165b expressions, and cell apoptosis in hRVECs. The interaction and localization of the RNA-binding protein alternative splicing factor/splicing factor 2 (ASF/SF2) were assessed using RNA pull-down. Although VEGF165 expression decreased, VEGF165b levels increased significantly in hRVECs transfected with rs3025039, which decreased cell viability and induced apoptosis. The SNPs rs3025033 and rs10434 had no significant effects on VEGF165b protein production and apoptosis; however, they promoted cell proliferation. SNPs affected the interaction between RNA and ASF/SF2, a splicing factor for intron retention. Insulin-like growth factor-1 treatment induced the expression of VEGF165, but not VEGF165b, whereas SRPIN340 treatment, an inhibitor of ASF/SF2, increased VEGF165b protein levels. VEGF gene sequence variations affected hRVEC proliferation and apoptosis via alternative gene splicing. Thus, the regulation of splicing via ASF/SF2 could be a potential strategy in treating pathological neovascularization in patients with AMD.

Effective splicing localization based on noise level inconsistencies

In the era of digital technology, it becomes easy to share photographs and videos using smartphones and social networking sites to their loved ones. On the other hand, many photo editing tools evolved to make it effortless to alter multimedia content. It makes people accustomed to modifying their photographs or videos either for fun or extracting attention from others. This altering brings a questionable validity and integrity to the kind of multimedia content shared over the internet when used as evidence in Journalism and Court of Law. In multimedia forensics, intense research work is underway over the past two decades to bring trustworthiness to the multimedia content. This paper proposes an efficient way of identifying the manipulated region based on Noise Level inconsistencies of spliced mage. The spliced image segmented into irregular objects and extracts the noise features in both pixel and residual domains. The manipulated region is then exposed based on the cosine similarity of noise levels among pairs of individual objects. The experimental results reveal the effectiveness of the proposed method over other state-of-art methods.

A Quaternion Two‐Stream R‐CNN Network for Pixel‐Level Color Image Splicing Localization

Recently, Zhou et al. designed a two-stream faster Region-Convolutional neural networks (R-CNN) model RGB-N for color image splicing localization in CVPR2018. However, the RGB-N locates spliced regions only at block-level and ignores the entirety and inherent correlation of three channels. Therefore, an improved quaternion two-stream R-CNN model is proposed to solve these drawbacks: a mask branch combining fully convolutional network and condition random field is added for locating spliced regions at pixel-level; quaternion representation of color images is used to process color spliced images in a holistical way. In addition, feature pyramid network based on quaternion residual network is considered to extract multi-scale features for color spliced images; attention region proposal network is combined with attention mechanism and is designed to pay more attention to the spliced regions; a high-pass filter designed for image splicing detection specifically is adopted to replace steganalysis rich model filter in the RGB-N to obtain noise input for the noise stream. Experimental results on a new synthetic dataset and three standard forgery datasets demonstrate that the proposed method is superior to the existing methods in the abilities of localization, generalization, and robustness.

Detection and Localization of Multiple Image Splicing Using MobileNet V1

In modern society, digital images have become a prominent source of information and medium of communication. They can, however, be simply altered using freely available image editing software. Two or more images are combined to generate a new image that can transmit information across social media platforms to influence the people in the society. This information may have both positive and negative consequences. Hence there is a need to develop a technique that will detect and locates a multiple image splicing forgery in an image. This research work proposes multiple image splicing forgery detection using Mask R-CNN, with a backbone as a MobileNet V1. It also calculates the percentage score of a forged region of multiple spliced images. The comparative analysis of the proposed work with the variants of ResNet is performed. The proposed model is trained and tested using our MISD (Multiple Image Splicing Dataset), and it is observed that the proposed model outperforms the variants of ResNet models (ResNet 51,101 and 151).