Image Forgery Detection Research Articles

A Literature Review on IMAGE FORGERY DETECTION

A Literature Review on IMAGE FORGERY DETECTION

State-of-the-Art Techniques for Image Forgery Detection: A Review

State-of-the-Art Techniques for Image Forgery Detection: A Review

Copy-Move Forgery Detection Using Texture Features of Hidden Forged Regions

The recent revolution in technology has not only eased our daily activities at work and home but also introduced new threats. In their daily activities, people exchange a lot of files such as text files, images, videos, etc. that can be used for a variety of purposes. One of the most common types of files is images. These kinds of files can be used to socialize people or spread knowledge among communities. Some of the exchanged images are fake or forged which can lead to the spread of misinformation, which is dangerous. This paper tries to suggest a method for image forgery detection that is copy-move-based. This means a part of the image is used to hide or change other parts in the same image. The suggested method divides an image into several blocks. The feature vectors of the blocks are extracted using a modified Gabor filter. The extracted features are, then, reduced using the principal component analysis technique. The next step is to match the blocks and extract similar ones (duplicated blocks). The findings show that the suggested method is efficient compared to other methods in the literature in terms of detection rate and false positive detection. Also, the proposed method detected forged regions of images when having a 60% of compression rate.

Convolutional block attention based network for copy-move image forgery detection

Convolutional block attention based network for copy-move image forgery detection

Enhanced blind image forgery detection using an accurate deep learning based hybrid DCCAE and ADFC

Enhanced blind image forgery detection using an accurate deep learning based hybrid DCCAE and ADFC

Performance Evaluation of Local Binary Patterns LBP for Copy-Move Forgery Detection in Digital Images: A Comparative Study

Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

Robust Image Forgery Detection Over Online Social Network Shared Images

In servers and mobile users, an image is sent over the social network or exchanged. Due to the fact that it contains delicate personal information, the privacy of that data is crucial. A hacker may use social information about a person to discredit them if their image is hacked. Text-based encryption can be used in mobile cloud computing under the current architecture. There are various ways to store data securely utilizing mobile computing, including end-to-end encryption of data transmission and dynamic credential generation that only generates text. We'll be creating a brand-new wavelet watermarking technique called the discrete wavelet transform for use in real-time social network applications like Facebook, and this study suggests an efficient image forgery detection method that recognizes a manipulated foreground or background. Using this technique, images can be used and safely kept on servers. We categorize the image as either common or delicate, and we also enhance the project to include copy right implementation. Run copyright algorithms referred to as wavelet transform algorithms when employing sensitive techniques. After that, provide the receiver secure access to download the pictures. Using C#.NET as the front end and SQL SERVER as the back end, experimental results show the efficiency of current algorithms in real-time social network contexts and a comparison of their privacy rates

Copy-Move Forgery Detection Using an Equilibrium Optimization Algorithm (CMFDEOA)

Image forgery detection is a new challenge. One type of image forgery is a copy-move forgery. In this method, part of the image is copied and placed at the most similar point. Given the existing algorithms and processing software, identifying forgery areas is difficult and has created challenges in various applications. The proposed method based on the Equilibrium Optimization Algorithm (EOA) helps image forgery detection by finding forgery areas. The proposed method includes feature detection, image segmentation, and detection of forgery areas using the EOA algorithm. In the first step, the image converts to a grayscale. Then, with the help of a discrete cosine transform (DCT) algorithm, it is taken to the signal domain. With the help of discrete wavelet transform (DWT), its appropriate properties are introduced. In the next step, the image is divided into blocks of equal size. Then the similarity search is performed with the help of an equilibrium optimization algorithm and a suitable proportion function. Copy-move forgery detection using the Equilibrium Optimization Algorithm (CMFDEOA) can find areas of forgery with an accuracy of about 86.21% for the IMD data set and about 83.98% for the MICC-F600 data set.

Image Forgery Detection based on Fusion of Lightweight Deep Learning Models

Image Forgery Detection based on Fusion of Lightweight Deep Learning Models

High-performance image forgery detection via adaptive SIFT feature extraction for low-contrast or small or smooth copy–move region images

High-performance image forgery detection via adaptive SIFT feature extraction for low-contrast or small or smooth copy–move region images

Comparison of specific segmentation methods used for copy move detection

<p><span lang="EN-US">In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature.</span></p>

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.

An Improved VGG16 and CNN-LSTM Deep Learning Model for Image Forgery Detection

As the field of image processing and computer vision continues to develop, we are able to create edited images that seem more natural than ever before. Identifying real photos from fakes has become a formidable obstacle. Image forgery has become more common as the multimedia capabilities of personal computers have developed over the previous several years. This is due to the fact that it is simpler to produce fake images. Since image object fabrication might obscure critical evidence, techniques for detecting it have been intensively investigated for quite some time. The publicly available datasets are insufficient to deal with these problems adequately. Our work recommends using a deep learning based image inpainting technique to create a model to detect fabricated images. To further detect copy-move forgeries in images, we use an CNN-LSTM and Improved VGG adaptation network. Our approach could be useful in cases when classifying the data is impossible. In contrast, researchers seldom use deep learning theory, preferring instead to depend on tried-and-true techniques like image processing and classifiers. In this article, we recommend the CNN-LSTM and improved VGG-16 convolutional neural network for intra-frame forensic analysis of altered images.

ASCA-squeeze net: Aquila sine cosine algorithm enabled hybrid deep learning networks for digital image forgery detection

The basic element in resolving numerous challenges, particularly social concerns like those in court cases and Facebook, is image forgery detection. The primary objective of this study is to build and develop an effective system for detecting digital image forgery utilising the recently proposed technique called the Aquila Sine Cosine Algorithm (ASCA). The forgery from the digital image is detected in this study using a hybrid deep learning technique that incorporates Deep Convolutional Neural Network (DCNN) and Squeeze Net. Additionally, the training time and computational complexity of the detection process are decreased by updating the weight of both the DCNN and the Squeeze Net using the developed ASCA technique. Additionally, the developed ASCA is produced by combining the update functions of the Aquila Optimizer (AO) with the Sine Cosine Algorithm (SCA). As a result, the hybrid deep learning classifier provides the classified output as either the authentic image or the forged image using a copy-move forgery detection dataset. The experimentation of the developed model has provided higher performance, as shown by testing accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) of 0.980, 0.976, and 0.956, respectively. Furthermore, by varying the iteration, testing accuracy, TNR, and TPR obtained by the devised technique are 0.944, 0.947, and 0.936, and by varying the population size obtained testing accuracy values of 1, TNR values of 1.003, and TPR values of 0.991, respectively, by algorithmic analysis.

DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery

In this paper, we propose a lightweight deep learning network (DRRU-Net) for image-splicing forgery detection. DRRU-Net is an architecture that combines RRU-Net for learning the visual content of images and image acquisition artifacts, and a JPEG artifact learning module for learning compression artifacts in the discrete cosine transform (DCT) domain. The backbone model of a network based on pre-training, such as CAT-Net, a representative network for image forgery detection, has a relatively large number of parameters, resulting in overfitting in a small dataset, which hinders generalization performance. Therefore, in this paper, the learning module is designed to learn the characteristics according to the DCT domain in real time without pre-training. In the experiments, the proposed network architecture and training method of DRRU-Net show that the network parameters are smaller than CAT-Net, the forgery detection performance is better than that of RRU-Net, and the generalization performance for various datasets can be improved.

SNIS: A Signal Noise Separation-Based Network for Post-Processed Image Forgery Detection

Image forgery detection has aroused widespread research interest in both academia and industry because of its potential security threats. Existing forgery detection methods achieve excellent tampered regions localization performance when forged images have not undergone post-processing, which can be detected by observing changes in the statistical features of images. However, forged images may be carefully post-processed to conceal forgery boundaries in a particular scenario. It becomes tough challenging to these methods. In this paper, we perform an analogous analysis between image forgery detection and blind signal separation, and formulate the post-processed image forgery detection problem into a signal noise separation problem. We also propose a signal noise separation-based (SNIS) network to solve the problem of detecting post-processed image forgery. Specifically, we first adopt the signal noise separation module to separate tampered region from the complex background region with post-processing noise, which weakens or even eliminates the negative impact of post-processing on forgery detection. Then, the multi-scale feature learning module uses a parallel atrous convolution architecture to learn high-level global features from multiple perspectives. Besides, a feature fusion module is utilized to enhance the discriminability of tampered regions and real regions by strengthening the boundary information. Finally, the prediction module is designed to predict the tampered region and classify the type of tampering operation. Extensive experiments show that the proposed SNIS is not only effective for forgery detection on forged images without post-processing, but also promising in robustness against multiple post-processing attacks. Furthermore, SNIS is robust in detecting forged images from unknown sources.

A Versatile Detection Method for Various Contrast Enhancement Manipulations

Contrast enhancement manipulation is a common method to improve the visual effect of an image. Meanwhile, it can also be considered a type of global image forgery because it changes the image’s visual appearance without alerting its semantics. Moreover, for local image forgery, a tampered image may be composited by images with different contrast enhancement manipulations or post-processed by a contrast enhancement manipulation to conceal the trails of tampering. Therefore, contrast enhancement manipulation detection is critical to global image forgery detection. The existing methods can only detect a particular type of contrast enhancement manipulation, such as gamma correction or histogram equalization. To break this limitation, we propose the zero-gap spans (ZGS) as the fingerprint to explore the traces of contrast enhancement manipulations. Based on ZGS, various contrast enhancement manipulations can be distinguished by a simple classification method at image-level and patch-level; different gamma corrections can be identified, and their gamma value can be estimated. Experimental results indicate that the proposed ZGS-based classification method can achieve and maintain good classification performance under different cases (gamma correction, simple histogram equalization, modified histogram equalization techniques). Meanwhile, ZGS can estimate the gamma value with the mean squared error (MSE) below 0.1156. For the local forgery images, the proposed ZGS also can be utilized to locate the regions with different contrast enhancement manipulations.

ForensicNet: Modern convolutional neural network-based image forgery detection network.

The advancements in Image editing techniques produce realistic-looking artificial images with ease. These images can easily circumvent the forensic systems making the authentication process more tedious and difficult. To overcome this problem, we introduce a modern convolutional neural network (CNN) named ForensicNet, inspired by the recent developments in computer vision. The three major contributions of our CNNs are inverted bottleneck, separate downsampling layers, and using depth-wise convolutions for mixing information in the spatial dimension. The inverted bottlenecks help improve accuracy and reduce network parameters/FLOPs. The separate downsampling layers help converge the network. The normalization layers also help stabilize training whenever the spatial resolution is changed. The depth-wise convolution is a grouped convolution where the number of groups and channels are the same. The experiments show that ForensicNet outperforms the state-of-the-art methods by a large margin.

A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network

Recently, digital images have been considered the primary key for many applications, such as forensics, medical diagnosis, and social networks. Image forgery detection is considered one of the most complex digital image applications. More profoundly, image splicing was investigated as one of the common types of image forgery. As a result, we proposed a convolutional neural network (CNN) model for detecting splicing forged images in real-time and with high accuracy, with a small number of parameters as compared with the recently published approaches. The presented model is a lightweight model with only four convolutional layers and four max-pooling layers, which is suitable for most environments that have limitations in their resources. A detailed comparison was conducted between the proposed model and the other investigated models. The sensitivity and specificity of the proposed model over CASIA 1.0, CASIA 2.0, and CUISDE datasets are determined. The proposed model achieved an accuracy of 99.1% in detecting forgery on the CASIA 1.0 dataset, 99.3% in detecting forgery on the CASIA 2.0 dataset, and 100% in detecting forgery on the CUISDE dataset. The proposed model achieved high accuracy, with a small number of parameters. Therefore, specialists can use the proposed approach as an automated tool for real-time forged image detection.

A fast copy-move image forgery detection approach on a reduced search space

A fast copy-move image forgery detection approach on a reduced search space