Image Forgery Detection Research Articles

Detecting Post Editing of Multimedia Images using Transfer Learning and Fine Tuning

In the domain of general image forgery detection, a myriad of different classification solutions have been developed to distinguish a “tampered” image from a “pristine” image. In this work, we aim to develop a new method to tackle the problem of binary image forgery detection. Our approach builds upon the extensive training that state-of-the-art image classification models have undergone on regular images from the ImageNet dataset, and transfers that knowledge to the image forgery detection space. By leveraging transfer learning and fine tuning, we can fit state-of-the-art image classification models to the forgery detection task. We train the models on a diverse and evenly distributed image forgery dataset. With five models—EfficientNetB0, VGG16, Xception, ResNet50V2, and NASNet-Large—we transferred and adapted pre-trained knowledge from ImageNet to the forgery detection task. Each model was fitted, fine-tuned, and evaluated according to a set of performance metrics. Our evaluation demonstrated the efficacy of large-scale image classification models—paired with transfer learning and fine tuning—at detecting image forgeries. When pitted against a previously unseen dataset, the best-performing model of EfficientNetB0 could achieve an accuracy rate of nearly 89.7%.

The Optimal Model for Copy-Move Forgery Detection in Medical Images.

Digital devices can easily forge medical images. Copy-move forgery detection (CMFD) in medical image has led to abuses in areas where access to advanced medical devices is unavailable. Forgery of the copy-move image directly affects the doctor's decision. The method discussed here is an optimal method for detecting medical image forgery. The proposed method is based on an evolutionary algorithm that can detect fake blocks well. In the first stage, the image is taken to the signal level with the help of a discrete cosine transform (DCT). It is then ready for segmentation by applying discrete wavelet transform (DWT). The low-low band of DWT, which has the most image properties, is divided into blocks. Each block is searched using the equilibrium optimization algorithm. The blocks are most likely to be selected, and the final image is generated. The proposed method was evaluated based on three criteria of precision, recall, and F1 and obtained 90.07%, 92.34%, and 91.56%, respectively. It is superior to the methods studied on medical images. It concluded that our method for CMFD in the medical images was more accurate.

Passive Image Forgery Detection Techniques: A Review, Challenges, and Future Directions

Passive Image Forgery Detection Techniques: A Review, Challenges, and Future Directions

Image Forgery Detection Based on Parallel Convolutional Neural Networks

Abstract— Due to the availability of deep networks, progress has been made in the field of image recognition. Images and videos are spreading very conveniently and with the availability of strong editing tools the tampering of digital content become easy. To detect such scams, we proposed techniques. In our paper, we proposed two important aspects of employing deep convolutional neural networks to image forgery detection. We first explore and examine different preprocessing method along with convolutional neural networks (CNN) architecture. Later we evaluated the different transfer learning for pre-trained ImageNet(via-fine-tuning) and implement it over our dataset CASIA V2.0. So, it covers the pre-processing techniques with basic CNN model and later see the powerful effect of the transfer learning models. Keywords— image tampering, convolution neural network (CNN), error level analysis (ELA), transfer learning, sharpening filter, fine-tuning

Squirrel Henry Gas Solubility Optimization driven Deep Maxout Network with multi‐texture feature descriptors for digital image forgery detection

SummaryThe ability to detect image forgeries is a crucial component in solving many problems, especially social ones that arise in legal proceedings, on Facebook, and so forth. Generally, forgery detection attempts to predict the artifacts through evaluating the differences in texture properties of an image. One division of an image is copied in a comparable image, typically at a different position, in a process known as copy move forgery. The two main types of copy move forgery prediction are keypoint‐based and block‐based techniques. While the block‐based approach takes more time, the keypoint‐based model performs poorly. The goal of this research was to develop a method for detecting image counterfeiting called the Squirrel Henry Gas Solubility Optimization algorithm‐based Deep Maxout Network (SHGSO‐based DMN). Here, Steerable Pyramid Transform (SPT) decomposition is done in order to obtain quantity of multi‐scale and multi‐oriented sub bands. For the purpose of detecting forgeries, the Deep Maxout Network which was constructed using the SHGSO model is deployed. With accuracy of 0.9507, True Negative Rate (TNR) of 0.9568, and True Positive Rate (TPR) of 0.9517, the new method outperformed other current forgery detection techniques.

Exploring the Effectiveness of Machine Learning Algorithms in Image Forgery Detection

This study investigates the efficacy of various machine learning algorithms for detecting image forgery, a prevalent issue in the realm of digital media manipulation. The research focuses on assessing the performance of these algorithms in accurately identifying instances of image tampering, aiming to contribute valuable insights to the field of digital forensics. The evaluation encompasses a diverse set of machine learning techniques, including but not limited to convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees. Through rigorous experimentation and comparative analysis, the research aims to discern the strengths and limitations of each algorithm in the context of image forgery detection. The findings of this study hold significance for enhancing the capabilities of digital forensics tools, thereby aiding in the mitigation of fraudulent activities, and ensuring the integrity of visual content in the digital' domain.

Deep learning-based efficient and robust image forgery detection

Deep learning-based efficient and robust image forgery detection

Image Forgery Detection on Multi-Resolution Splicing Attacks using DCT and DWT

Image Forgery Detection on Multi-Resolution Splicing Attacks using DCT and DWT

Towards JPEG-Resistant Image Forgery Detection and Localization Via Self-Supervised Domain Adaptation.

With wide applications of image editing tools, forged images (splicing, copy-move, removal and etc.) have been becoming great public concerns. Although existing image forgery localization methods could achieve fairly good results on several public datasets, most of them perform poorly when the forged images are JPEG compressed as they are usually done in social networks. To tackle this issue, in this paper, a self-supervised domain adaptation network, which is composed of a backbone network with Siamese architecture and a compression approximation network (ComNet), is proposed for JPEG-resistant image forgery detection and localization. To improve the performance against JPEG compression, ComNet is customized to approximate the JPEG compression operation through self-supervised learning, generating JPEG-agent images with general JPEG compression characteristics. The backbone network is then trained with domain adaptation strategy to localize the tampering boundary and region, and alleviate the domain shift between uncompressed and JPEG-agent images. Extensive experimental results on several public datasets show that the proposed method outperforms or rivals to other state-of-the-art methods in image forgery detection and localization, especially for JPEG compression with unknown QFs.

Development of an Approach for Image Forgery Detection Using Machine Learning Algorithms

Digital picture fraud detection is an increasing societal necessity due to the importance of verified images. The detection of picture copying, splicing, retouching, and re-sampling forgeries is included. In the absence of digital signatures or watermarks, passive picture authentication may serve as an alternative to active authentication. Passive techniques, every so often recognized as blind techniques, could take place without preceding knowledge of the picture or its reference. Identifying counterfeiting picture or tampering was a research field for long a period of time, triggered via the Internet, online platforms, social messaging platforms, and extensive digital image usage. The rate of failure could be a key factor for examining the alteration of picture or forgery, among other existing methods. The research applies almost six common algorithms related to machine learning in order to extract features from Lightweight, Spatial Exploitation, and Residual deep learning models on benchmark datasets MICC-F220, Columbia, and CoMoFoD. The models of incorporated deep learning could consist of AlexNet, GoogleNet, VGG16, VGG19, SqueezeNet, MobileNetV2, ShuffleNet, ResNet-18, ResNet-50, and ResNet-101 for spatial exploitation. Fine-tuning is applied to the top three deep learning models, optimizing hyperparameters centered on indicators of performance for every single benchmark dataset. Tweaked SqueezeNet, MobileNetV2, and ShuffleNet deep learning models with SGDM Optimizer and SVM classifier yielded the best results for MICC-F220 dataset. Fine-tuned VGG19, MobileNetV2, and ResNet-50 deep learning models with SGDM Optimizer and SVM v classifier yielded the best results for Columbia dataset. In CoMoFoD dataset, fine-tuned AlexNet, MobileNetV2, and ShuffleNet deep learning models with SGDM Optimizer and SVM classifier yielded the best results. The proposed approach, utilizing machine learning algorithms and deep learning features, enhanced forgery detection and reduced false positives. Results were validated on benchmark image forgery datasets and compared to current methods.

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

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.

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.

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.

Copy-Move Forgery Detection in Medical Images Using Handcrafted Features

Abstract: In the realm of medical imaging, the authenticity and integrity of images are paramount for accurate diagnosis and treatment planning. Copy-move forgery, a prevalent form of image tampering, poses a significant threat to the reliability of medical images. This research project focuses on the development and implementation of a robust copy-move forgery detection system tailored specifically for medical images. The proposed methodology leverages handcrafted features, extracting distinctive characteristics from the images to detect instances of forgery. Through a meticulous process of feature engineering and selection, the algorithm aims to enhance sensitivity and specificity in identifying manipulated regions within medical images. The study explores the application of advanced image processing techniques and pattern recognition algorithms to achieve a high level of accuracy in forgery detection.

A generalized novel image forgery detection method using generative adversarial network

A generalized novel image forgery detection method using generative adversarial network