Image Forgery Research Articles

Dual-Stream Intermediate Fusion Network for Image Forgery Localization

Dual-Stream Intermediate Fusion Network for Image Forgery Localization

An Intelligent Approach to Classify and Detection of Image forgery attack (Scaling and Cropping) using Transfer Learning

An Intelligent Approach to Classify and Detection of Image forgery attack (Scaling and Cropping) using Transfer Learning

Deep learning-based image forgery detection system

Deep learning-based image forgery detection system

Cyber vaccinator for image tamper resilient and recovery using invertible neural network

People frequently interact with their families, friends, and colleagues through Online Social Networks (OSNs). People post and share their photos in online communities and content-sharing sites. The problem addressed in this paper is the susceptibility of digital images to tampering, which compromises security and privacy. Traditional image forgery detection methods face challenges in reproducing original content after manipulation. This paper introduces an advanced Image Immunization System leveraging Invertible Neural Networks. The system, which comprises the cyber vaccinator, vaccine validator, forward pass for tamper detection, and backward pass for image self-recovery, aims to proactively immunize images against various attacks. The run-length encoding in the backward pass transforms hidden perturbations into information, facilitating the recovery of the authentic image. The middleware's expansion to multimodal content analysis, including videos and audio, provides a more comprehensive defense against digital manipulation within OSNs. These advancements reflect a commitment to robust security and holistic content integrity. The Cyber Vaccinator, using Invertible Neural Networks (INNs) for image tamper resilience and recovery, demonstrates significant effectiveness in detecting tampering and restoring images, providing a robust solution for maintaining image integrity. The Cyber Vaccinator uses an Invertible Neural Network (INN) to safeguard image integrity. It detects tampering by analyzing invariant features and responds with precise recovery methods. By continuously monitoring images, it ensures real-time tamper detection and efficient restoration, maintaining image authenticity through advanced neural network resilience and recovery techniques.

Enhanced Image Forgery Detection using a Hybrid Approach: Integration of ELA, CNN, and XGBoost

Enhanced Image Forgery Detection using a Hybrid Approach: Integration of ELA, CNN, and XGBoost

Weber Law Based Approach for Multi-Class Image Forgery Detection

Weber Law Based Approach for Multi-Class Image Forgery Detection

Detecting Digital Image Forgeries with Copy-Move and Splicing Image Analysis using Deep Learning Techniques

Detecting Digital Image Forgeries with Copy-Move and Splicing Image Analysis using Deep Learning Techniques

Image forgery detection on multi-resolution splicing attacks using DCT and DWT

Image forgery detection on multi-resolution splicing attacks using DCT and DWT

Image Forgery Detection using Adaptive Oversegementation and Key Point Matching

Abstract: The scope of this paper is to propose a image forgery detection application by combining adaptive oversegmentation and feature point matching, the suggested scheme presents a novel method for detecting copy-move forgery in images. Using superpixel-based analysis, it divides the image into erratic blocks, extracts feature points, and fine-tunes areas that could be fakes. The accuracy of the detected forged areas is then improved by morphological operations. Results from experiments indicate that this method performs better than current ones, especially under difficult circumstances.

FSM and RRU-net module for Image Splicing Forgery Detection

Nowadays, it can be difficult to tell whether an image is real or fake. Thanks to technological advancements, an image can be altered or falsified in a matter of seconds. Finding these forgeries has grown to be a major problem in the modern world. Although an image could be crucial evidence, it will be useless if it is faked. Methods for distinguishing between pictures that have been edited and those that have been computer-generated must be developed. In order to identify these forgeries, we plan to create an Image Forgery Detection Model that combines FSM and RRU-Net. Residual propagation and residual feedback are two distinct approaches that are combined in RRU-Net, which stands for Ringed Residual Structure and Network Architecture. To find long-distance dependencies, the Feature Similarity Module, or FSM, will be employed. Our suggested system combines FSM and RRU-Net to improve accuracy. We will extract the differences in the picture attributes between the modified and unmodified parts using image patches of different sizes. Once the forged area has been identified, the final region will be shown in color. The method will prove useful in the future for identifying different types of spliced image frauds that appear on different social media platforms.

A Deep Learning Model for Splicing Image Detection

With the advancement of digital technology, manipulating images has become relatively easy through many photo editing techniques. One of the techniques is the splicing image method, which crops parts of images and puts them into another image creating a new composite image. The image splicing detection system is soon regarded as an exciting topic for many researchers to solve the problems of forgery images on the Internet, especially in social networks. ResNet-50 and VGG-16 are powerful architectures of convolutional neural networks, but they reveal many weaknesses when operating on low-end computers. The ultimate goal of this research is to create a model for image splicing detection working well in limited memory machines. The study proposes the model, which is the improvement of VGG-16 applying residual network (ResNet). As a result, the proposed model achieves a test accuracy of 92.5% while the ResNet-50 gives an accuracy of 85.6% after 20 epochs of training 9,319 images from the CASIA v2.0 dataset, which are used for forgery classification. The result proves the efficiency of the proposed model for image splicing detection, especially when working on low-end computers.

Harmonizing Image Forgery Detection & Localization: Fusion of Complementary Approaches.

Image manipulation is easier than ever, often facilitated using accessible AI-based tools. This poses significant risks when used to disseminate disinformation, false evidence, or fraud, which highlights the need for image forgery detection and localization methods to combat this issue. While some recent detection methods demonstrate good performance, there is still a significant gap to be closed to consistently and accurately detect image manipulations in the wild. This paper aims to enhance forgery detection and localization by combining existing detection methods that complement each other. First, we analyze these methods' complementarity, with an objective measurement of complementariness, and calculation of a target performance value using a theoretical oracle fusion. Then, we propose a novel fusion method that combines the existing methods' outputs. The proposed fusion method is trained using a Generative Adversarial Network architecture. Our experiments demonstrate improved detection and localization performance on a variety of datasets. Although our fusion method is hindered by a lack of generalization, this is a common problem in supervised learning, and hence a motivation for future work. In conclusion, this work deepens our understanding of forgery detection methods' complementariness and how to harmonize them. As such, we contribute to better protection against image manipulations and the battle against disinformation.

Image Splicing Forgery Detection using Error Level Analysis and CNN

The issue of image forgery through splicing has become increasingly relevant in the current digital era. Splicing involves the manipulation of images by combining parts of two or more different images to create a deceptive composite image. This technique can be employed for various purposes, including the dissemination of false information, damaging someone's reputation, or even creating confusion in specific contexts. Several techniques used to detect splicing involve statistical analysis, color analysis, and texture analysis. Additionally, artificial intelligence developments, such as deep learning, have been applied to enhance detection capabilities. In this study, we employed a Convolutional Neural Network (CNN) model to identify image deviations caused by splicing. Optimization was performed on the convolutional layers of the model to improve CNN performance. The integration of Error Level Analysis (ELA) was also implemented to aid in identifying splicing forgeries, where portions of one image are combined with parts of another. Areas that have undergone splicing may exhibit noticeable differences in error levels. The dataset utilized for this research was sourced from DVMM and CUISDE. The validation accuracy results for our CNN model before incorporating ELA were 61% for DVMM and 74% for CUISDE. After adding ELA, the CNN model demonstrated improved detection accuracy, achieving validation rates of 72% for DVMM and 71% for CUISDE.

DSSE-net: dual stream skip edge-enhanced network with forgery loss for image forgery localization

DSSE-net: dual stream skip edge-enhanced network with forgery loss for image forgery localization

Modeling of intelligent hyperparameter tuned deep learning based copy move image forgery detection technique

Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters.

Image forgery detection using deep neural networks: current state-of-the-art and future directions

Image forgery detection using deep neural networks: current state-of-the-art and future directions

A generalized novel image forgery detection method using generative adversarial network

A generalized novel image forgery detection method using generative adversarial network

Improved image forgery localization method from a statistical perspective

Improved image forgery localization method from a statistical perspective

GP-Net: Image Manipulation Detection and Localization via Long-Range Modeling and Transformers

With the rise of image manipulation techniques, an increasing number of individuals find it easy to manipulate image content. Undoubtedly, this presents a significant challenge to the integrity of multimedia data, thereby fueling the advancement of image forgery detection research. A majority of current methods employ convolutional neural networks (CNNs) for image manipulation localization, yielding promising outcomes. Nevertheless, CNN-based approaches possess limitations in establishing explicit long-range relationships. Consequently, addressing the image manipulation localization task necessitates a solution that adeptly builds global context while preserving a robust grasp of low-level details. In this paper, we propose GPNet to address this challenge. GPNet combines Transformer and CNN in parallel which can build global dependency and capture low-level details efficiently. Additionally, we devise an effective fusion module referred to as TcFusion, which proficiently amalgamates feature maps generated by both branches. Thorough extensive experiments conducted on diverse datasets showcase that our network outperforms prevailing state-of-the-art manipulation detection and localization approaches.

Deep Learning-based Copy-Move and Spliced Image Forgery Detection

This paper proposes a Deep Learning (DL) based pre-trained AlexNet model for detecting and localizing copy-move and spliced forgery in photos. To localise forgeries in a photo, a binary mask is constructed using sobel operators. Further feature vectors are extracted patch wise from the input pictures. The Spatial Rich Model (SRM) is employed to address the generalisation issues in the DL model. There are three datasets used: Columbia Uncompressed Image Splicing Detection Evaluation (CUISDE), CASIA 1, and CASIA 2. The overall performance of the model has a 98.59 percent total accuracy as against 98.176% reported in the existing literature.