MMFD-Net: A Novel Network for Image Forgery Detection and Localization via Multi-Stream Edge Feature Learning and Multi-Dimensional Information Fusion

Effective Copy-Move Forgery Detection (CMFD) in digital images tackles the increasing issue of image authenticity stemming from developments in image editing tools. The study devised a comprehensive approach that amalgamates Deep Learning (DL) with conventional techniques to improve the identification of copy-move fraud in digital photos, employing the CASIA v2.0 dataset for assessment. A deep Convolutional Neural Network (CNN) is employed for feature extraction, producing high-dimensional feature vectors that encapsulate the complex attributes of the images. The vectors are subsequently subdivided into training and testing sets, facilitating the training of multiple models, including VGG16 (Visual Geometry Group of the University of Oxford), AdaBoost, and Vision Transformers (ViT). Each model is assessed using measures like accuracy, precision, recall, and F1-score, enabling the identification of the most successful model for classifying photos as original or forged. Experimental findings indicated that AdaBoost attained the maximum accuracy at 99.50%, followed by ViT at 98.20%, and VGG16 at 89.30%. AdaBoost's enhanced efficacy, achieved by integrating the predictions of multiple base classifiers to improve accuracy, suggests its viability as the most dependable technique for CMFD.

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.

The increasing accessibility of digital imaging technology has led to a rise in image forgery, raising concerns about digital content authenticity in forensic and security domains. Copy-move forgery is one the most prevalent and challenging forgery techniques due to its seamless manipulation. We propose a novel passive CMFD (CMFD) approach that leverages a modified Kirsch (mKirsch) edge detector and a modified SIFT-based descriptor (DivSIFT) to accurately identify and localize copy-move forgery (CMF). The mKirsch edge detector enhances edge detection by selectively deleting specific masks, improving keypoint extraction and feature matching. We used MICC-F220, CoMoFoD, and MICC-F8Multi datasets to measure the performance of the new method, under challenging conditions such as rotation, scaling, JPEG compression, and multiple forgeries. The results show that mKirsch-enhanced detection outperforms compared to conventional Kirsch-based methods. Notably, methods with deleted masks (WW_NW and NE_SE) achieved a True Positive Rate (TPR) of 90.91%, precision 100%, and an F-measure of 95.24%. Robust against rotation and scaling attacks, achieving a TPR of up to 96.97% with zero false positives. Additionally, the method is computationally efficient, with an execution time of 2.74 seconds, making it suitable for real-world applications. These findings establish the mKirsch-based CMFD as a highly accurate and efficient solution for image forgery detection in digital images.

In recent years, digital image forgery detection has become an active research area due to the advancement of photo editing software. This paper focuses on passive forgery detection on images tampered using copy move technique, better known as Copy Move Forgery Detection (CMFD). A CMFD technique consisting of oriented Features from Accelerated Segment Test and rotated Binary Robust Independent Elementary Features (Oriented FAST and rotated BRIEF) as the feature extraction method and 2 Nearest Neighbour (2NN) with Hierarchical Agglomerative Clustering (HAC) as the feature matching method is proposed. Evaluation of the proposed CMFD technique was performed on images that underwent various geometrical attacks. With the proposed technique, an overall accuracy rate of 84.33% and 82.79% are obtained for evaluation carried out with images from the MICC-F600 and MICC-F2000 databases. Forgery detection achieved True Positive Rate of more than 91% for tampered images with object translation, different degree of rotation and enlargement.

A passive blind digital image forgery detection method was proposed in this paper. Basic defocus model shows that image patches with similar distances to the lens have similar blur kernel sizes. This consistency is broken in image forgery as the result of possible blurring and different imaging conditions. Our forgery detection technique uses local blur estimation at each edge pixels to exposes the defocus blur inconsistency. Experiment results of tampered images from real law cases show the effectiveness of our technique.

Digital image forgery detection using passive techniques: A survey

The rise in sophisticated image forgery techniques, driven by advancements in image editing and generation, has posed new security challenges. Traditional methods, designed for specific tampering artifacts, struggle with out-of-distribution image forgery detection. In this paper, we propose a shift in paradigm, placing greater emphasis on the universal characteristics of authentic images, as opposed to solely focusing on specific forgery signals. We introduce an enhancement to the Masked Autoencoder (MAE), aptly termed the Forgery MAE (FMAE). This modification retains the inherent characteristics of natural images while integrating multi-source forgery information. Our implementation involves applying the lottery ticket hypothesis during pre-training to identify forgery-sensitive parameters, followed by their sparse fine-tuning to target the forgery detection and localization task. Concurrently, we develop a ``mixture of experts'' noise extractor to compile multi-source forgery data. Our FMAE effectively extracts forgery features and shows strong resilience against unseen forgeries. Extensive experiments across multiple datasets confirm our method's superior accuracy and generalization capability over existing techniques.

A survey on deep learning-based image forgery detection

ELA-Conv: Forgery Detection in Digital Images Based on ELA and CNN

Multi-semantic CRF-based attention model for image forgery detection and localization

In this paper, an adaptive method for copy-move forgery detection and localization in digital images is proposed. The method employs wavelet transform with non constant Q factor and characterizes image pixels through the multiscale behavior of corresponding wavelet coefficients. The detection of forged regions is then performed by considering similar those pixels having the same multiscale behavior. The method is pointwise and the length of pixel features vector is image dependent, allowing for a more precise and fast detection of forged regions. The qualitative and quantitative evaluation of the experimental results reveals that the proposed method outperforms some existing transform-based methods in terms of performance and execution time.

Image forgery detection by transforming local descriptors into deep-derived features

Most of the existing image forgery detection methods are limited to binary classification or the detection of a single forgery type, making them unsuitable for comprehensive analysis. This article proposes a novel multi-class image forgery detection strategy capable of categorizing images into five classes: authentic, retouched, geometrically manipulated, copy-move forged, and spliced. The framework employs hybrid features comprising elliptical upper and lower ternary pattern histograms, SURF bins, and Network-Based Dimensionality Reduction (NBDR), combined with an Artificial Neural Network (ANN) classifier optimized using the Red-Tailed Hawk Algorithm (RTHA). Experimental evaluation on a dataset of 2,500 authentic and forged images demonstrated the superior performance of the proposed method, achieving 94.32% accuracy, 98.58% precision, 94.32% recall, 94.36% F1-score, and an AUC of 0.965. These results confirm that the integration of hybrid features with RTHA-optimized ANN classification offers a robust and reliable solution for multi-class digital image forgery detection.

Digital images are widely used nowadays and there are powerful and convenient software tools for various types of image processing and enhancement. Unfortunately, these tools also facilitate for easy digital image forgery. In this paper we present an algorithm for digital image forgery detection that deals with the situation when some object, together with its shadow, is copied and pasted to some other location in the same or different image. Algorithm is based on the property that shadows do not change the texture of the underlaying surface. Our algorithms uses local binary patterns from shadow and adjacent non-shadow regions and features extracted from their histograms where energy and entropy proved to be the most discriminative. The proposed method was tested on some benchmark forged images and compared with other approaches from literature where it proved to be successful in detection of this type of forgery.

Statistical feature-based pattern recognition approach has been proved to be useful in digital image forgery detection. In this paper, we first present this approach adopted in Phase 1 of the first IEEE IFS-TC Image Forensics Challenge, in which the task is to classify the tampered images from the original ones, together with the experimental results. Several different kinds of statistical features and their combinations have been tested. Finally, we have chosen to use co-occurrence matrices calculated from the rich model of noise residual images. Furthermore we have selected a subset of the rich model to further enhance the testing accuracy by about 2 % so as to reach a high detection accuracy of 93.7 %. In Phase 2 of the competition, the task is to localize the tampered regions by identifying the tampered pixels. For this purpose, we have introduced the Hamming distance of Local Binary Patterns as similarity measure to tackle the tampering without post-processing on copy-moved regions. The PatchMatch algorithm has been adopted as an efficient search algorithm for block-matching. We have also applied a simple usage of the scale-invariant feature transform (SIFT) when other kinds of processing such as rotation and scaling have been performed on the copy-moved region. The achieved f-score in identifying tampered pixels is 0.267. In summary, some success has been achieved apparently. However, much more efforts are called for to move image tampering detection ahead.