Tampering Traces Research Articles

Data tampering detection and recovery scheme based on multi-branch target extraction for internet of vehicles

With the rapid development of new networks such as 5 G/6 G, the Internet of Vehicles has been given features such as hyper-connectivity and hyper-intelligence, promoting the implementation of new scenarios for autonomous vehicles. However, on the Internet of vehicles, vehicles use many cameras, radars and other sensors to sense the environment and execute instructions, facing security issues such as road data tampering and hijacking. Consequently, this paper presents a data tampering detection and recovery scheme based on multi-branch target extraction. Specifically, for road images collected by sensors, this paper presents a target extraction method based on multi-branch spatial feature pyramid blocks to obtain road salient targets. Then, a tamper detection and recovery algorithm based on interval mapping is presented. Once the image road target is tampered with, this method can quickly detect the tampering traces and restore them to achieve the authenticity and integrity of the road image on the Internet of Vehicles. The availability of the proposed scheme is verified through comparative experiments, and the performance is improved satisfactorily compared with other works.

A two-branch encoder-decoder network for image tampering localization

Tampered images with false information can mislead viewers and pose security issues. Tampering traces in images are difficult to detect. To locate tampering traces effectively, a dual-domain deep-learning-based image tampering localization method based on RGB and frequency stream branches is proposed in this work. The former branch learns and extracts tampered features on the image and content features of the tampered region. The latter branch extracts tampered features from the frequency domain to complement the RGB stream branch. In addition, an attention mechanism is used to integrate the features from both branches at the fusion stage. In the experiments, the F1 score of the proposed method outperformed those of the baselines on the NIST16 dataset (with a 15.3 % improvement), and the AUC score outperformed those of the baselines on the NIST16 and COVERAGE datasets (improvements of 3.9 % and 4.7 %, respectively). This study provides a beneficial alternative to image tampering localization techniques.

Image manipulation detection and localization using multi-scale contrastive learning

Current image tampering detection methods rely on various forgery footprints, such as JPEG artifacts and edge inconsistencies, and use algorithms related to image segmentation. However, these methods have several issues, including over-fitting, focusing on only a few specific forgery footprints, and emphasizing semantically relevant information while ignoring tampering traces. This paper proposes a model for image manipulation detection and localization based on multi-scale contrast learning(MSCL-Net). The model utilizes the differences in feature distributions between tampered and untampered regions to extract a comprehensive tamper trace. It uses a dual-stream structured encoder that incorporates both RGB raw images and SRM noise features. A Feature Cross-Fusion Module (FCFM) is proposed to fuse features for improving feature representation of tampered information. The decoding process involves the use of an Adaptive Self-Attention Module (ASAM) to filter and aggregate relevant context from coarse feature maps. Additionally, a Supervised Contrastive Learning Module (SCLM) is used to expand the difference between tampered and untampered areas. The loss function for multi-loss fusion comprises classification loss, segmentation loss, and multi-scale supervised contrastive loss. This improves the network's understanding of global differences, reduces false positives, weakens semantic information, and enhances the model's ability to locate tampered regions of varying sizes. Extensive experiments are conducted across multiple datasets, demonstrating that our model is robust against attacks and resilient to false-positive predictions at both the image-level and pixel-level. Furthermore, its overall performance exceeds that of state-of-the-art alternatives in reliably detecting and localizing tampered images.

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.

Unveiling tampering traces: Enhancing image reconstruction errors for visualization

Contemporary tampering detection models in digital image forensics often rely on established techniques such as SRM and ELA for revealing evidence of manipulation. Despite their widespread use, these methods often yield unsatisfactory visualizations. In order to enhance the visibility of tampering artifacts, we propose an innovative image representation called Tamper Reconstruction Error (TRE). TRE measures the error between an input image and its reconstructed counterpart using a pre-trained mixed generator. We observed that utilizing a model proficient in computational visual tasks to extract reconstruction errors did not clearly reveal tampering traces in manually manipulated images. To emphasize the more pronounced discrepancies in the reconstruction of tampered images, the image representation TRE is fed into two dedicated extractors designed to capture manipulation features in both the frequency and spatial domains. Throughout the learning process, these extractors adaptively express the essential forgery traces back into spatial domain. Furthermore, to validate the importance of the extracted errors in tampering localization, we introduced a localization annotator. This annotator integrates reconstruction errors at different stages during the encoding and decoding of latent features. Experimental results demonstrate that the integration of extracted features significantly improves the performance of tampering localization, outperforming other state-of-the-art localization frameworks.

Learning Discriminative Noise Guidance for Image Forgery Detection and Localization

This study introduces a new method for detecting and localizing image forgery by focusing on manipulation traces within the noise domain. We posit that nearly invisible noise in RGB images carries tampering traces, useful for distinguishing and locating forgeries. However, the advancement of tampering technology complicates the direct application of noise for forgery detection, as the noise inconsistency between forged and authentic regions is not fully exploited. To tackle this, we develop a two-step discriminative noise-guided approach to explicitly enhance the representation and use of noise inconsistencies, thereby fully exploiting noise information to improve the accuracy and robustness of forgery detection. Specifically, we first enhance the noise discriminability of forged regions compared to authentic ones using a de-noising network and a statistics-based constraint. Then, we merge a model-driven guided filtering mechanism with a data-driven attention mechanism to create a learnable and differentiable noise-guided filter. This sophisticated filter allows us to maintain the edges of forged regions learned from the noise. Comprehensive experiments on multiple datasets demonstrate that our method can reliably detect and localize forgeries, surpassing existing state-of-the-art methods.

EMF-Net: An edge-guided multi-feature fusion network for text manipulation detection

Text images have a more uniform background than natural images, making it difficult to capture tampering traces. Thus, it poses significant challenges to the detection and localization of tampered text images. To effectively detect tampered text images, we propose the edge-guided multi-feature fusion network (EMF-Net) including four different modules, the difference semantic discriminator (DSD), the edge-guidance feature aggregation module (EFAM), the edge supervised module (ESM), and the multi-branch attention-induced fusion module (MAFM). The proposed network combines RGB images and learned noise-sensitive features to deeply excavate the hidden tampered region features to improve the detection accuracy due to the insufficient feature information. Meanwhile, the multi-branch attention-induced fusion and semantic discriminator methods are integrated to reduce false alarms by shielding the feature interference in non-tampered regions and diminish the external interference. Furthermore, we craft a dataset of 12,000 text images and their tempered versions with three tempering operations including copy-move, splicing and inpainting. Extensive experiments have shown that the proposed network can improve the generalization performance and achieve the higher detection accuracy compared to current other state-of-the-art methods.

Uncovering visual attention-based multi-level tampering traces for face forgery detection

Uncovering visual attention-based multi-level tampering traces for face forgery detection

DMFF-Net: Double-stream multilevel feature fusion network for image forgery localization

With the rapid development of image processing technology, it has become increasingly easy to manipulate images, which poses a threat to the stability and security of people’s lives. Recent methods have proposed the fusion of RGB and noise features to uncover tampering traces. However, these approaches overlook the characteristics of features at different levels, leading to insufficient feature fusion. To address this problem, this paper proposes a double-stream multilevel feature fusion network (DMFF-Net). Unlike the traditional feature fusion approach, DMFF-Net adopts a graded feature fusion strategy. It classifies features into primary, intermediate, and advanced levels and introduces the Primary Feature Fusion Module (PFFM) and the Advanced Feature Fusion Module (AFFM) to achieve superior fusion results. Additionally, a multisupervision strategy is employed to decode the fused features into level-specific masks, including boundary, regular, and refined masks. The DMFF-Net is validated on publicly available datasets, including CASIA, Columbia, COVERAGE, and NIST16, as well as a real-life manipulated image dataset, IMD20, and achieves AUCs of 84.7%, 99.6%, 86.6%, 87.4% and 82.8%, respectively. Extensive experiments show that our DMFF-Net outperforms state-of-the-art methods in terms of image manipulation localization accuracy and exhibits improved robustness.

Dual-branch multi-scale densely connected network for image splicing detection and localization

In the last two decades, numerous methods have been proposed for image tampering forensics, especially when deep learning is leveraged as a powerful feature extraction technique. However, existing deep learning-based methods are not efficient to jointly explore robust representations of tampering traces from both low-level forensics patterns and high-level forensics semantics. Besides, they become unreliable for spliced regions with unknown scales in the evaluation stage. To overcome the above-mentioned limitations, a novel Dual-branch Multi-scale Densely Connected Network (DMDC-Net) is proposed in this work for image splicing detection. DMDC-Net is constructed based on an encoder-decoder framework, which equips a multi-scale dense (MD) module for feature reuse and pixel-wise feature fusion (PFF) module to leverage forensics clues from different data components adaptively. In DMDC-Net, the given input image will be fed into two individual subnets to obtain deep representation maps from RGB and high-frequency components, which contains both visual and noise inconsistencies for further analysis. Besides, the MD module is applied in Encoder of each subnet to make the model jointly learn low-level forensics patterns and high-level semantic representations, which incorporates local and global features by a multi-scale manner of dense connections. The outputs of each subnet are fused in the PFF module to take advantage of their complementarity by trainable weights and get the final detection score map. Experimental results demonstrate the effectiveness of our method and the superior performance than other state-of-the-art methods in CASIA v2.0 and Columbia dataset for different scenarios, such as post-processing operations.

PL-GNet: Pixel Level Global Network for detection and localization of image forgeries

Unlike most Image Forgery Detection and Localization (IFDL) methods that classify the tampered regions by local patch, the features from the whole image in the spatial and frequency domains are leveraged in this paper to classify each pixel in the image. This paper proposes a high-confidence pixel level global network called PL-GNet to combat real-life image forgery that commonly involves different types and visually looks particularly realistic. There are three building blocks in our end-to-end PL-GNet framework: (1) An Encoding net allows us to extract the global features and generate the high-quality feature maps which indicate possible tampered regions. The newly designed first layer and backbone network architecture based on atrous convolution in Encoding net are adopted to capture the changes of pixel relationships and extract rich multi-scale spatial information. (2) A Long Short Term Memory (LSTM) network based on co-occurrence matrix is designed to capture the tampering traces and the discriminative features between manipulated and non-manipulated regions. (3) A Decoding net that incorporates the output of Encoding net and LSTM network learns the mapping from low-resolution feature maps to pixel-wise prediction masks. Furthermore, we conduct a series of ablation experiments to optimize the design of the Encoding network systematically. Extensive experiments on the six challenging datasets demonstrate that our PL-GNet outperforms each subnetwork and consistently achieves state-of-the-art performance compared to alternative methods over three evaluation metrics.

Enhanced features in image manipulation detection

Image editing technology leads to tampering traces hidden in images hard to be identified, which results in great challenges for image manipulation detection. Therefore, it is necessary to enhance the characteristic response of tampering traces. In this paper, a two-stream network is exploited to extract tampering features from both semantic level and noise level through end-to-end training. At the input end of the network, a multi-scale high-frequency constrained convolution kernel module is proposed to extract multi-channel and multi-scale noise fingerprint information, which enhances the tampering traces. Moreover, we leverage multi-task learning to realize the classification of tampering techniques, the detection and segmentation of tampering regions. The tampered segmentation mask attention is introduced as a novel constraint to be fed back to the detection branch, which further enhances the tampering characteristics and improves the detection performance. Experiments on COVER, CASIA and NIST16 datasets prove that the proposed framework outperforms existing methods quantitatively and qualitatively. The proposed method effectively enhances the characteristics of tampered regions. Moreover, the proposed method has high robustness on JPEG recompression and image resize.

Exposing Deepfake Face Forgeries With Guided Residuals

For Deepfake detection, residual-based features can preserve tampering traces and suppress irrelevant image content. However, inappropriate residual prediction brings side effects on detection accuracy. Meanwhile, residual-domain features are easily affected by some image operations such as lossy compression. Most existing works exploit either spatial-domain or residual-domain features, which are fed into the backbone network for feature learning. Actually, both types of features are mutually correlated. In this work, we propose an adaptive fusion based guided residuals network (AdapGRnet), which fuses spatial-domain and residual-domain features in a mutually reinforcing way, for Deepfake detection. Specifically, we present a fine-grained manipulation trace extractor (MTE), which is a key module of AdapGRnet. Compared with the prediction-based residuals, MTE can avoid the potential bias caused by inappropriate prediction. Moreover, an attention fusion mechanism (AFM) is designed to selectively emphasize feature channel maps and adaptively allocate the weights for two streams. Experimental results show that AdapGRnet achieves better detection accuracies than the state-of-the-art works on four public fake face datasets including HFF, FaceForensics++, DFDC and CelebDF. Especially, AdapGRnet achieves an accuracy up to 96.52% on the HFF-JP60 dataset, which improves about 5.50%. That is, AdapGRnet achieves better robustness than the existing works.

Image manipulation detection by multiple tampering traces and edge artifact enhancement

Image manipulation detection has attracted considerable attention owing to the increasing security risks posed by fake images. Previous studies have proven that tampering traces hidden in images are essential for detecting manipulated regions. However, existing methods have limitations in generalization and the ability to tackle post-processing methods. This paper presents a novel Network to learn and Enhance Multiple tampering Traces (EMT-Net), including noise distribution and visual artifacts. For better generalization, EMT-Net extracts global and local noise features from noise maps using transformers and captures local visual artifacts from original RGB images using convolutional neural networks. Moreover, we enhance fused tampering traces using the proposed edge artifacts enhancement modules and edge supervision strategy to discover subtle edge artifacts hidden in images. Thus, EMT-Net can prevent the risks of losing slight visual clues against well-designed post-processing methods. Experimental results indicate that the proposed method can detect manipulated regions and outperform state-of-the-art approaches under comprehensive quantitative metrics and visual qualities. In addition, EMT-Net shows robustness when various post-processing methods further manipulate images.

Multi-Feature Fusion Based Deepfake Face Forgery Video Detection

With the rapid development of deep learning, generating realistic fake face videos is becoming easier. It is common to make fake news, network pornography, extortion and other related illegal events using deep forgery. In order to attenuate the harm of deep forgery face video, researchers proposed many detection methods based on the tampering traces introduced by deep forgery. However, these methods generally have poor cross-database detection performance. Therefore, this paper proposes a multi-feature fusion detection method to improve the generalization ability of the detector. This method combines feature information of face video in the spatial domain, frequency domain, Pattern of Local Gravitational Force (PLGF) and time domain and effectively reduces the average error rate of span detection while ensuring good detection effect in the library.

ET: Edge-Enhanced Transformer for Image Splicing Detection

A key challenge of image splicing detection is how to localize integral tampered regions without false alarm. Although current forgery detection approaches have achieved promising performance, the integrality and false alarm are overlooked. In this paper, we argue that the insufficient use of splicing boundary is a main reason for poor accuracy. To tackle this problem, we propose an Edge-enhanced Transformer (ET) for tampered region localization. Specifically, to capture rich tampering traces, a two-branch edge-aware transformer is built to integrate the splicing edge clues into the forgery localization network, generating forgery features and edge features. Furthermore, we design a feature enhancement module to highlight the artifacts of the edge area in forgery features and assign weight values to the resulting tensor in spatial domain for vital signal strengthening and noise suppression. Extensive experimental results on CASIA v1.0, CASIA v2.0 and NC2016 demonstrate that the proposed method can accurately localize tampered regions in both pixel and edge levels and outperforms state-of-the-art methods.

First step towards parameters estimation of image operator chain

Many effective techniques have recently been proposed to estimate the parameters of specific tampering operations. Most of them consider the situation in which an image is tampered with by only a single operation. However, in reality, multiple operations are used to falsify an image. Because the tampering traces of previous operations may be weakened or eliminated by later operations, it is difficult for the prior algorithms, each of which was developed for a single operation, to detect all tampering operations. In this paper, we propose a new method for estimating the parameters of operations in different manipulation chains. A framework is presented to investigate the correlation between multiple operations, which divides the degree of correlation into uncoupled and coupled operations. Then, two cases of certain operator chains with different degrees of correlation are adopted to reveal the assessment framework. Under this framework, we design well-directed features to estimate the parameters for each operator chain. Finally, the experimental results demonstrate the effectiveness of the proposed framework.

General Purpose Image Tampering Detection using Convolutional Neural Network and Local Optimal Oriented Pattern (LOOP)

Digital image tampering detection has been an active area of research in recent times due to the ease with which digital image can be modified to convey false or misleading information. To address this problem, several studies have proposed forensics algorithms for digital image tampering detection. While these approaches have shown remarkable improvement, most of them only focused on detecting a specific type of image tampering. The limitation of these approaches is that new forensic method must be designed for each new manipulation approach that is developed. Consequently, there is a need to develop methods capable of detecting multiple tampering operations. In this paper, we proposed a novel general purpose image tampering scheme based on CNNs and Local Optimal Oriented Pattern (LOOP) which is capable of detecting five types of image tampering in both binary and multiclass scenarios. Unlike the existing deep learning techniques which used constrained pre-processing layers to suppress the effect of image content in order to capture image tampering traces, our method uses LOOP features, which can effectively subdue the effect image content, thus, allowing the proposed CNNs to capture the needed features to distinguish among different types of image tampering. Through a number of detailed experiments, our results demonstrate that the proposed general purpose image tampering method can achieve high detection accuracies in individual and multiclass image tampering detections respectively and a comparative analysis of our results with the existing state of the arts reveals that the proposed model is more robust than most of the exiting methods.

Image Tampering Localization Using a Dense Fully Convolutional Network

The emergence of powerful image editing software has substantially facilitated digital image tampering, leading to many security issues. Hence, it is urgent to identify tampered images and localize tampered regions. Although much attention has been devoted to image tampering localization in recent years, it is still challenging to perform tampering localization in practical forensic applications. The reasons include the difficulty of learning discriminative representations of tampering traces and the lack of realistic tampered images for training. Since Photoshop is widely used for image tampering in practice, this paper attempts to address the issue of tampering localization by focusing on the detection of commonly used editing tools and operations in Photoshop. In order to well capture tampering traces, a fully convolutional encoder-decoder architecture is designed, where dense connections and dilated convolutions are adopted for achieving better localization performance. In order to effectively train a model in the case of insufficient tampered images, we design a training data generation strategy by resorting to Photoshop scripting, which can imitate human manipulations and generate large-scale training samples. Extensive experimental results show that the proposed approach outperforms state-of-the-art competitors when the model is trained with only generated images or fine-tuned with a small amount of realistic tampered images. The proposed method also has good robustness against some common post-processing operations.

An Image Tampering Detection Algorithm of Qualification Certificate Based on CNN and SVM

With the high speed development of digital image processing technology, the phenomenon of malicious tampering with certificate images is rampant. Facing the problems of difficult human eye recognition of tampering traces and poor detection and classification effect of CNN model, a tampering detection algorithm based on CNN combined with SVM is proposed. The algorithm first performs data preprocessing of image compression, difference, resise, and normalization on the image. Then it uses the CNN model to extract the tamper feature, finally completes the calculation and classification of the fully connected layer feature data through SVM, and builds a certified image CNN+SVM model with tamper detection capability. Experimental results show that comparing with the traditional human eye recognition method and the existing deep learning CNN model, the algorithm can reach a detection accuracy of 48.09%, 91.79 %, and 98.67% under the self-built data set of tampering with certificate images. The detection accuracy rate is higher than the previous two detection accuracy rates.