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Powerful image editing software makes the process of image manipulation easy, which increases security risks. Therefore, it is urgent to locate the tampered region to uncover the processing history. However, previous research has mainly focused on feature extraction, with little discussion on classifiers for classifying original and tampered regions. We improve the splicing forgery localization method from a statistical perspective. The refined color filter array feature provides sufficient data for statistical analysis, and the geometric mean is used to eliminate anomalous data. Subsequently, a classifier that combines the expectation–maximization algorithm and Bayesian theory is proposed to binarize the original and tampered regions. The two steps of feature extraction and feature classification are associated from a statistical perspective, which ultimately improve the performance of the method effectively. Extensive experimental results demonstrate that the refined feature used for classification has several advantages, and the proposed classifier is appropriate for handling complex image manipulation across different statistical distributions. The proposed method outperforms the reference methods in both the Columbia and Korus datasets. [ABSTRACT FROM AUTHOR]

The addition of objects to the original image in splice forgery results in a change in the semantics of the original image, and distribution of these spliced images may bring negative impacts. To solve this issue, many forgery detection methods based on convolutional neural networks are presented. However, they tend to extract deep features, but ignore the importance of shallow semantics. In addition, the complexity of the forgery localization task leads to insufficient accuracy in detecting smaller forged regions. Based on the above problems, an end-to-end image splicing localization network based on multi-scale features and a residual refinement module (RRM) is proposed in this work. Our approach can be roughly divided into two modules: the detection module and the RRM. First, shallow and deep features are extracted from a backbone network, and deep features are then processed by the deeper atrous spatial pyramid pooling (ASPP) module to extract multi-scale features. The deeper ASPP module uses a smaller dilation rate and light convolution, which is more suitable for detecting complex counterfeit images. Second, the shallow features are fused with the multi-scale features to complement the shallow semantic information, such as texture and edges, which further improve the robustness of the model. Finally, the detection network generates coarse prediction maps that are fed to the RRM, and the RRM optimizes these masks by smoothing the boundaries, filling small gaps, and enhancing the edge details, which improves the pixel-level segmentation results for forgery detection. Extensive experimental results on several public datasets show that this method outperforms other state-of-the-art methods in image forgery localization. [ABSTRACT FROM AUTHOR]

In the realm of picture forensics, it might be difficult to find and locate an image-splicing forgery. To improve the accuracy of the picture forensic evaluation, we introduce a dual encoder network (DAE-Net) with an efficient channel attention (ECA) module. The ECA module creates a fusion approach with an attention mechanism that enables the model to concentrate on local objects' tampering characteristics and increases the accuracy of multi-region tampering identification. We suggest combining a dual-coding network with a multi-scale dilated convolutional feature fusion module to better detect small target tampering zones. Experimental evidence suggests that DAE-Net outperforms state-of-the-art methods. The attack experiments also demonstrate the DEA-Net model's stability and noise resistance. [ABSTRACT FROM AUTHOR]

In this digital era, images are the essential medium of communication. They may be found on magazine covers, legal evidence, newspapers, and social media. With the advancement of numerous editing tools, image modification will pose a serious concern due to the spread of information. Image splicing is one of the leading strategies in image forgery. While there are several approaches to solving the problem, some have flaws, such as the accuracy of faked region localization. We present an effective image-splicing forgery localization model called VGG16Unet. The VGG16Unet is an encoder-decoder architecture for the localization of image-spliced forgery. The pre-trained VGG16 is used as an encoder part of the architecture, and our network's decoder part is inspired by UNet architecture. The faked images and their respective masks are sent over the network in form of patches to train the model. This model is tested on the widely used dataset of image splicing forgery, i.e., NIST and CASIA v2. The experimental result illustrates that VGG16Unet's performance of localizing the image-spliced forged regions is more effective than prior methods. [ABSTRACT FROM AUTHOR]

In multimedia forensics, several methods have been developed for the authentication of digital images. However, the detection and localization of removed objects from an image has always been a challenging problem. Image forgery, for the removal of objects, can be done in many ways. Among them, image inpainting performs object removal and fills the empty region with surrounding patches. The clues of inpainted region are visually imperceptible. Till date, limited work has been done for image inpainting detection. Hence, a convolutional neural network-based model for the detection of inpainted regions in an image is presented in this research. A hybrid encoder–decoder-based architecture is proposed, where a segment of DenseNet-121 architecture is adopted as an encoder. The primary goal of this architecture is to use spatial maps to explore the distinguishing features between inpainted and uninpainted regions. Inpainted image dataset created by using the exemplar-based image inpainting method is used to train and validate the proposed model. The performance of the proposed model is evaluated using various performance metrics. Experimental results show that the proposed model outperformed existing methods for a variety of inpainted images. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence, especially the application and development of deep learning, brings not only great convenience to people but also some challenges. Some face forgery techniques can confuse the false with the true in deep learning. With the widespread popularity of social applications and streaming media, people pay more and more attention to portrait privacy and security. To solve the great threat to personal privacy caused by deep face forgery technology, we propose an effective face forgery method to enrich the face antispoofing datasets. We first explore the representation of identity information in latent space. Based on this foundation, we use the identity feature replacement module to edit the latent codes in the latent space and finally generate the images with photo-realistic results with the help of the powerful generation ability of StyleGAN. The face forgery method can further expand the face antispoofing dataset, provide rich data for the learning-based forgery detection methods to improve their generalization and to ensure the security in preserving portrait privacy. [ABSTRACT FROM AUTHOR]

With the development of technology, it is becoming easier and easier for people to generate tampered images that are indistinguishable from the human eye, and the malicious use of these photos in news, academic papers, and criminal crimes has brought great harm to society. This paper proposes a deep convolutional neural network-based image forgery localization method to uncover the subtle differences between doctored images and real images. Specifically, the network achieves tampering localization through a three-stage enhancement scheme. First, the dilated convolution in the deep layer of the network is used to keep the feature map resolution constant, and the number of shallow convolutions is decreased to reduce the perceptual field, so the network focuses on local regions. Second, the feature enhancement module is used to fuse shallow features with deep features to effectively filter content information and highlight tampering features, making full use of local and global information to improve the generalization ability. Finally, the attention enhancement module reweights the convolutional feature maps in terms of channels and locations, respectively, to highlight the information regions around the forgery boundaries, thus guiding the network to capture more intrinsic features for image forgery. Extensive experimental results on several public datasets show that this method outperforms other state-of-the-art methods in image forgery localization. [ABSTRACT FROM AUTHOR]

Image manipulation is transformed into a big issue for data integrity. The use of advanced imaging technology expends the regularity of multimedia forgeries. To detect such forgeries, some effective forgery identification methods are proposed to estimate the 3D lighting fingerprints by making certain suppositions related to the surface and reflection model. Incident lighting dispersal in a scene provides a physics-based key for exposing image exploitations. The proposed technique is more relaxed for multiple light source-based forgery detection. Also, the novelty of the technique is that it considers heterogeneous image surfaces, nearby surface geometry, and texture information to assess the lighting environment. The Phong reflection model is implemented to estimate the structural profiles. The method works in two phases. In the first phase, pre-processing over selected patches is performed followed by angle and error estimations in the second phase. To identify such forgeries, elevation angles ϑ concerning mounted light sources are estimated. The value of the elevation angle is computed for various patches of the image. The proposed method is relevant for a wide range of objects present in the image, i.e., not only limited to head positions, etc. To build up and validate the technique, comprehensive testing on the synthetic image dataset is done. Synthetic images are designed under different intensity light sources. Experimental results demonstrate a better forgery detection accuracy compared with other state-of-the-art methods in this domain. Also, the proposed technique is tested for generalized forged images. A comparative analysis with other related techniques is done to validate the proposed method using a synthetic image dataset. The results are pertinent to all type of images, and an error threshold is limited to 8 degrees with respect to the illuminant position. [ABSTRACT FROM AUTHOR]

Passive forgery detection aims to detect traces of image tampering without the need for prior information. With the increasing demand for image content protection, passive detection methods able to identify image tampering areas are increasingly needed. However, most current passive approaches either work only for image-level JPEG compression detection and cannot localize region-level forgery, or suffer from high-false detection rates in localizing altered regions. This paper proposes an effective approach based on discrete cosine transform coefficient analysis for the detection and localization of altered regions of JPEG compressed images. This approach can also work with altered JPEG images resaved in JPEG compressed format with different quality factors. Experiments with various tampering methods such as copy-and-paste, image completion, and composite tampering, show that the proposed approach is able to effectively detect and localize altered areas and is not sensitive to image contents such as edges and textures. [ABSTRACT FROM AUTHOR]

The feature matching step plays a critical role during the copy-move forgery detection procedure. However, when several highly similar features simultaneously exist in the feature space, current feature matching methods will miss a considerable number of genuine matching feature pairs. To this end, we propose a clustering-based method to collect qualified matching features for the feature point-based methods. The proposed method can collect far more genuine matching features than existing methods do, and thus significantly improve the detection performance, especially for multiple pasting cases. Experimental results confirm the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Tampering related to document forgeries is often accomplished by copy-pasting or add-printing. These tampering methods introduce character distortion mutation in documents. We present a method of exposing document forgeries using distortion mutation of geometric parameters. We estimate distortion parameters, which consist of translation and rotation distortions, through image matching for each character. Detection of tampered characters with distortion mutation occurs based on a distortion probability, which is calculated from character distortion parameters. The introduction of a visualized probability map describes the degree of distortion mutation for a full page. The proposed method exposes the forgeries based on individual characters and applies to English and Chinese document examinations. Experimental results demonstrate the effectiveness of our method on low JPEG compression quality and low resolution. [ABSTRACT FROM AUTHOR]

Recompression-based image forensics has been a largely investigated area in the forensic community over recent years. This mainly involves detecting the number of different compressions in an image by exploiting certain artifacts left behind by the compression stages. The artifacts left in images by Joint Photography Experts Group (JPEG) compression coding operations play an important role in passive forensic analysis of images, which help in assessing the authenticity of the image. In this regard, most of the solutions proposed to date are restricted to double compression detection in JPEG images. However, in practical cases, this restriction may not always work. This is because an image having undergone multiple stages of compression, including triple or higher degrees of compression, will be "missed" by such forgery detection algorithms. To address this issue, we propose a deep convolutional neural network (CNN) model that detects both double and triple compression in JPEG images. The model is trained with the compression features specific to single, double, and triple JPEG compressions. The trained model is capable of determining the authenticity of an image by distinguishing between tampered and untampered image regions. Further, the proposed model localizes the forged regions within an image, distinguishing between double and triple compressed regions. Our experimental results establish the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

It is difficult to extract appropriate features of blurry digital video because of the low quality image for which traditional forgery detection methods are not effective. The video is often blurred due to various complicated conditions, such as limited bandwidth or low pixel complementary metal oxide semiconductor sensors. To address the feature extraction problem for blurry video, we explore one low rank theory to deblur video, which fuses multiple fuzzy kernels of key frame images by low rank decomposition. Further, we extract mean structural similarity features and information fidelity criterion features sequentially by the double detection mechanism to detect forgery points on blurred video frames. We can determine the forgery form of the video and locate the source replication area of the copy-move forgery. Experimental evaluation on two public video databases and videos that we recorded show that this method is robust in blurry video forgery detection, and compared with traditional video forgery detection methods, the efficiency is improved. [ABSTRACT FROM AUTHOR]

Region duplication is a simple and effective operation to create digital image forgeries, where a continuous portion of pixels in an image is copied and pasted to a different location in the same image. Many prior copy–move forgery detection methods suffer from their inability to detect the duplicated region, which is subjected to various geometric transformations. A keypoint-based approach is proposed to detect the copy–move forgery in an image. Our method starts by extracting the keypoints through a fast Hessian detector. Then the adaptive minimal–maximal suppression (AMMS) strategy is developed for distributing the keypoints evenly throughout an image. By using AMMS and a sped-up robust feature descriptor, the proposed method is able to deal with the problem of insufficient keypoints in the almost uniform area. Finally, the geometric transformation performed in cloning is recovered by using the maximum likelihood estimation of the homography. Experimental results show the efficacy of this technique in detecting copy–move forgeries and estimating the geometric transformation parameters. Compared with the state of the art, our approach obtains a higher true positive rate and a lower false positive rate. [ABSTRACT FROM AUTHOR]

We propose a new user authentication system based on spoken signatures, where online signature and speech signals are acquired simultaneously. The main benefit of this multimodal approach is better accuracy at no extra cost for the user in terms of access time or inconvenience. Another benefit lies in a better robustness against intentional forgeries due to the extra difficulty for the forger to produce both signals. We set up an experimental framework to measure these benefits on MyIDea, a realistic multimodal biometric database publicly available. More specifically, we evaluate the performance of state of the art modeling systems based on Gaussian mixture models (GMM) and hidden Markov models (HMM) applied independently to the pen and voice signal, where a simple rule-based score fusion procedure is used. We conclude that the best performance is achieved by the HMMs, provided that their topology is optimized on a per user basis. Furthermore, we show that more precise models can be obtained through the use of maximum a posteriori probability (MAP) training instead of the classically used expectation maximization (EM). We also measure the impact of multisession scenarios versus monosession scenarios, and the impact of skilled versus unskilled signature forgeries attacks. [ABSTRACT FROM AUTHOR]