Image Splicing Forgery Detection Research Articles

The ringed residual u-net with non-natural regions feature for image splicing forgery detection and localization

Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-to-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-to-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack.

Image splicing forgery detection: A review

Image splicing forgery detection: A review

Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

Dual encoder network with efficient channel attention refinement module for image splicing forgery detection

Dual encoder network with efficient channel attention refinement module for image splicing forgery detection

The Circular U-Net with Attention Gate for Image Splicing Forgery Detection

With the advent and rapid development of image tampering technology, it has become harmful to many aspects of our society. Thus, image tampering detection has been increasingly important. Although current forgery detection methods have achieved some success, the scale of the tampered areas in each forgery image are different, and previous methods do not take this into account. In this paper, we believe that the inability of the network to accommodate tampered regions of various sizes is the main reason for the low precision. To address the mentioned problem, we propose a neural network architecture called CAU-Net, which adds residual propagation and feedback, attention gate and Atrous Spatial Pyramid Pooling with CBAM to the U-Net. The Atrous Spatial Pyramid Pooling with CBAM can capture information from multiple scales and adapt to differently sized target areas. In addition, CAU-Net can solve the vanishing gradient issue and suppress the weight of untampered regions, and CAU-Net is an end-to-end network without redundant image processing; thus, it is fast to detect suspicious images. In the end, we optimize the proposed network structure by ablation study, and the experimental results and visualization results demonstrate that our network has a better performance on CASIA and NIST16 compared with state of the art methods.

Feature enhancement and supervised contrastive learning for image splicing forgery detection

Image forgery detection remains a challenging task due to the variation in the scale of the tampered areas and the forensic clues that are obscured by various post-processing operations. Most existing deep learning methods rely only on single-scale high-level features and ignore the correlations of the pixels in intra-image and inter-image. Due to this limitation, previous methods are considered unsuitable for multi-scale splicing forgery detection. To fill this gap, we propose a novel model for improving multi-scale splicing forged regions localization by utilizing multi-level multi-scale feature enhancement and pixel-level supervised contrastive learning. First, we introduce a multi-level multi-scale feature enhancement module (MFEM) to integrate the multi-level information and capture the multi-scale global contextual representation by embedding an improved atrous spatial pyramid pooling (ASPP) mechanism into the non-local module. It strengthens the capability of the model to sense multi-scale tampered regions. Second, the pixel-level supervised contrastive learning mechanism is designed to separately cluster the pixel representations of tampered and real regions within and across images. It improves intra-class compactness and inter-class separability of the pixel embedding space significantly and enhances feature expression capabilities. Third, we design a multi-loss progressive learning (MPL) strategy to integrate the complementary advantages of multi-loss functions to optimize the scale and position parameters of the tampered regions during the training process. Extensive experiments have shown that the proposed model outperforms state-of-the-art methods. It can effectively detect and segment multi-scale tampered regions, even for noisy and JPEG-compressed images.

Image splicing forgery detection using simplified generalized noise model

To deal with the problem of image forgery detection, many forensic tools have been proposed. Most existing tools perform efficiently in a supervised scenario with a large training set while their statistical performances cannot be analytically established. Also, limited research proposes statistical model-based detectors for image forensics, especially for image splicing forgery detection. Therefore, in this paper, we propose a training-free forensic detector with analytically statistical performance for splicing forgery detection. The detector is designed based on a simplified noise model of JPEG image which assumes the variance of pixels as a quadratic function of the expectation of pixels. The proposed simplified noise model is characterized by two parameters which can serve as camera fingerprints for image forgery detection. By employing the framework of hypothesis testing theory, a training-free Generalized Likelihood Ratio Test (GLRT) is designed, which ensures the high detection performance for a prescribed false alarm rate. Besides, detection threshold can be configured in the fashion independent of image content. Numerical results validate the accuracy of the simplified noise model and the effectiveness of the proposed detector.

Image splicing forgery detection by combining synthetic adversarial networks and hybrid dense U‐net based on multiple spaces

With the popularity of image editing tools, the originality and information security of images are facing serious threats. The most common threat is splicing forgery that copies a part of the area from one donor image to the acceptor one. Some research works were proposed to protect the image originality, whereas they are still difficult to apply in practice. There are two main reasons: (a) very limited data for learning models; (b) huge attribute differences between the donor and acceptor images. We propose two novel tasks to conquer the above challenges: Synthetic Adversarial Networks (SANs) and Hybrid Dense U-Net (HDU-Net). SAN finds the most secluded position for inserting tampered areas in an image by learning the association between scenes and objects, and can enlarge the original small data set by more than 40 times. We call the data set SF-Data generated by SAN. We combine the dense U-Net that detects the differences of the essential attributes of image with four spaces containing more available feature information to propose HDU-Net. Then, the synthetic data set SF-Data are used to train HDU-Net. We perform various attack experiments on several public data sets to demonstrate the effectiveness and robustness of our method.

Image Splicing Detection Scheme Using Surf and Mean-LBP Based Morphological Operations


 Tampering with images and changing them without giving any evidence has become very popular because of the presence of an enormous degree of intense altering device. The image forensics technique has grown popular for deciding whether a picture has been changed with a copy-move, splicing, or different forgery techniques. This paper present’s a novel passive image splicing forgery detection technique which includes three steps. Firstly, extracting an interest region (ROI) by using SURF descriptor (Speed up Robust Transform) Points is concerned based on the morphological procedure. Secondly, the Extraction of Mean-LBP (Mean Local Binary Pattern) Highlights on ROI is carried out. Lastly, a Classification of Mean- LBP characteristics is done with the SVM classifier. This novel method has shown the best outcome as SVM is used for classification. As well as it has shown a higher accuracy on the standard three datasets CASIA TIDE V1.0, CASIA TIDE V2.0, and Columbia University, revealed that current method has achieved higher accuracy of 97.9%, 98.2%, and 98.9% respectively. Finally, in terms of accuracy, the proposed SFD scheme outperformed the best recent works in this area.

DCU-Net: a dual-channel U-shaped network for image splicing forgery detection.

The detection and location of image splicing forgery are a challenging task in the field of image forensics. It is to study whether an image contains a suspicious tampered area pasted from another image. In this paper, we propose a new image tamper location method based on dual-channel U-Net, that is, DCU-Net. The detection framework based on DCU-Net is mainly divided into three parts: encoder, feature fusion, and decoder. Firstly, high-pass filters are used to extract the residual of the tampered image and generate the residual image, which contains the edge information of the tampered area. Secondly, a dual-channel encoding network model is constructed. The input of the model is the original tampered image and the tampered residual image. Then, the deep features extracted from the dual-channel encoding network are fused for the first time, and then the tampered features with different granularity are extracted by dilation convolution, and then, the secondary fusion is carried out. Finally, the fused feature map is input into the decoder, and the predicted image is decoded layer by layer. The experimental results on Casia2.0 and Columbia datasets show that DCU-Net performs better than the latest algorithm and can accurately locate tampered areas. In addition, the attack experiments show that DCU-Net model has good robustness and can resist noise and JPEG recompression attacks.

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).

Controlling Neural Learning Network with Multiple Scales for Image Splicing Forgery Detection

The guarantee of social stability comes from many aspects of life, and image information security as one of them is being subjected to various malicious attacks. As a means of information attack, image splicing forgery refers to copying some areas of an image to another image to hide the traces of the original information and leads to grave consequences. Image splicing forgery is extremely complex since the attributes of the two images subjected to the pasting and copying operations are greatly different. In order to solve the issue mentioned above, we propose a method by applying a neural learning network controlled by multiple scales (MCNL-Net) based on U-Net to identify whether an image has been tampered and to locate the tampered regions. Firstly, the learning capacity of MCNL-Net is enhanced by the combination of a residual propagation module and a residual feedback module. An ingenious strategy is designed to control the size of local receptive field in each building block of MCNL-Net. The strategy makes MCNL-Net able to achieve properties and superiorities of multi-scale structure and learn specified features. For further improving the detection performance of MCNL-Net, a block attention mechanism is proposed to control the advanced degree of the input information in each building block. In addition, a MaxBlurPool method is applied into image splicing forgery detection for the first time, preserving the shift-equivariance of a convolutional neural network. Through experiments, we demonstrate that MCNL-Net can achieve more promising results and offer stronger robustness than the state-of-the-art splicing forgery detection methods.

Image splicing detection technique based on Illumination-Reflectance model and LBP

A Copy-create digital image forgery is image tampering that merges two or more areas of images from different sources into one composite image; it is also known as image splicing. Excellent forgeries are so tricky that they are not noticeable to the naked eye and don’t reveal traces of tampering to traditional image tamper detection techniques. To tackle this image splicing detection problem, machines learning-based techniques are used to instantly discriminate between the authentic and forged image. Numerous image forgery detection methods to detect and localize spliced areas in the composite image have been proposed. However, the existing methods with high detection accuracy are computationally expensive since most of them are based on hybrid feature set or rely on the complex deep learning models, which are very expensive to train, run on expensive GPUs, and require a very large amount of data to perform better. In this paper, we propose a simple and computationally efficient image splicing forgery detection that considers a trade-off between performance and the cost to the users. Our method involves the following steps: first, luminance and chrominance are found from the input image; second, illumination is estimated from Luminance using Illumination-Reflectance model; third, Local Binary Patterns normalized histogram for illumination and Chrominance is computed and used as the feature vector for classification using the following machine learning algorithms: Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Naive Bayes. Extensive experiments on the public dataset CASIA v2.0 show that the new algorithm is computationally efficient and effective for image splicing tampering detection.

Image Splicing Forgery Detection Scheme Using New Local Binary Pattern Varient

In this research develop passive image splicing detection method based on a new descriptor called Adaptive Threshold Mean Ternary Pattern (ATMTP). It was developed based on strength and weaknesses of both Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). ATMTP extraction feature is normally achieved by using proposed mean based thresholding and adaptive ternary thresholding, the former is robust to noise while the latter is robust to noise and other photometric attacks. It is designed to withstand against photometric manipulations, be it single or double attacks. In this research the ATMTP color features extracted from R, G, and B channels have revealed that the present method achieved higher accuracy on standard datasets CASIA V2.0 out of 99.03%, Sensitivity 99.6%, and specificity 98.1%. Finally, in terms of accuracy, the proposed SFD scheme outperformed the best recent works in this area.

Deep Learning on Digital Image Splicing Detection Using CFA Artifacts

Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images based on the analysis of color filter array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection Evaluation Dataset (CISDED) under different scenarios including adding postprocessing on the spliced images such JPEG compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with 95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the superiority of the proposed scheme compared to other recent splicing detection method.

Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

The image is the best information carrier in the current digital era and the easiest to manipulate. Image manipulation causes the integrity of this information carrier to be ambiguous. The image splicing technique is commonly used to manipulate images by fusing different regions in one image. Over the last decade, it has been confirmed that various structures in science and engineering can be demonstrated more precisely by fractional calculus using integrals or derivative operators. Many fractional-order-based techniques have been used in the image-processing field. Recently, a new specific fractional calculus, called conformable calculus, was delivered. Herein, we employ the combination of conformable focus measures (CFMs), and focus measure operators (FMOs) in obtaining redundant discrete wavelet transform (RDWT) coefficients for improving the image splicing forgery detection. The process of image splicing disorders the content of tampered image and causes abnormality in the image features. The spliced region’s boundaries are usually blurring to avoid detection. To make use of the blurred information, both CFMs and FMOs are used to calculate the degree of blurring of the tampered region’s boundaries for image splicing detection. The two public image datasets IFS-TC and CASIA TIDE V2 are used for evaluation of the proposed method. The obtained results of the proposed method achieved accuracy rate 98.30% for Cb channel on IFS-TC image dataset and 98.60% of the Cb channel on CASIA TIDE V2 with 24-D feature vector. The proposed method exhibited superior results compared with other image splicing detection methods.

Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering

This paper proposes a splicing forgery detection method with two parts: a coarse-to-refined convolutional neural network (C2RNet) and diluted adaptive clustering. The proposed C2RNet cascades a coarse convolutional neural network (C-CNN) and a refined CNN (R-CNN) and extracts the differences in the image properties between un-tampered and tampered regions from image patches with different scales. Further, to decrease the computational complexity, an image-level CNN is introduced to replace patch-level CNN in C2RNet. The proposed detection method learns the differences of various image properties to guarantee a stable detection performance, and the image-level CNN tremendously decreases its computational time. After the suspicious forgery regions are located by the proposed C2RNet, the final detected forgery regions are generated by applying the proposed adaptive clustering approach. The experiment results demonstrate that the proposed detection method achieves relatively promising results compared with state-of-the-art splicing forgery detection methods, even under various attack conditions.

Digital Image Forensics Based on CFA Interpolation Feature and Gaussian Mixture Model

According to the characteristics of the color filter array interpolation in a camera, an image splicing forgery detection algorithm based on bi-cubic interpolation and Gaussian mixture model is proposed. The authors make the assumption that the image is acquired using a color filter array, and that tampering removes the artifacts due to a demosaicing algorithm. This article extracts the image features based on the variance of the prediction error and create image feature likelihood map to detect and locate the image tampered areas. The experimental results show that the proposed method can detect and locate the splicing tampering areas precisely. Compared with bi-linear interpolation, this method can reduce the prediction error and improve the detection accuracy.

Image splicing forgery detection based on low-dimensional singular value decomposition of discrete cosine transform coefficients

Digital image forgery has significantly increased due to the rapid development of several tools of image manipulation. Based on the manipulation used to produce a tampered image, image forgery techniques can be characterized into three types: copy–move forgery, image splicing, and image retouching. Image splicing is achieved by adding regions from one image into another. This technique changes the content of the target image and causes variations in image features which are used to detect the forgery regions. In this study, an image splicing forgery detection method based on low-dimensional singular value decomposition of discrete cosine transform (DCT) coefficients has been presented. The suspicious input image is divided into multi-size blocks, and each block is transformed into 2D DCT. The DCT coefficients are calculated correspondingly to each block. The features from DCT are extracted using SVD algorithm. The roughness measure is calculated for the set of singular values obtained. Lastly, four types of statistical features—mean, variance, third-order moment skewness, and fourth-order moment kurtosis—are extracted from SVD features and are then arranged in a feature vector. Feature reduction has been applied by kernel principal component analysis. Finally, support vector machine is used to distinguish between the authenticated and spliced images. The proposed method was evaluated against three standard image datasets CASIA v1, DVMM v1, and DVMM v2. The proposed method shows an average detection accuracy of 97.15, 99.30, and 96.97 for DVMM v1, CASIA v1, and DVMM v2, respectively. These results outperform several current image splicing detection methods.

Image Splicing Forgery Detection Using Local Binary Pattern and Shift Vector

Image Splicing Forgery Detection Using Local Binary Pattern and Shift Vector