Forgery Detection Method Research Articles

Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations

This study proposes a novel forgery detection method based on the analysis of frequency components of images using the Discrete Fourier Transform (DFT). In recent years, face manipulation technologies, particularly Generative Adversarial Networks (GANs), have advanced to such an extent that their misuse, such as creating deepfakes indistinguishable to human observers, has become a significant societal concern. We reviewed two GAN architectures, StyleGAN and StyleGAN2, generating synthetic faces that were compared with real faces from the FFHQ and CelebA-HQ datasets. The key results demonstrate classification accuracies above 99%, with F1 scores of 99.94% for Support Vector Machines and 97.21% for Random Forest classifiers. These findings underline the fact that performing frequency analysis presents a superior approach to deepfake detection compared to traditional spatial detection methods. It provides insight into subtle manipulation cues in digital images and offers a scalable way to enhance security protocols amid rising digital impersonation threats.

Deep Learning Methods for Copy Move Image Forgery Detection: A Review

Deep Learning Methods for Copy Move Image Forgery Detection: A Review

An efficient convolution neural network method for copy-move video forgery detection

Unmanned systems play a pivotal role in military surveillance, critical infrastructure protection, law enforcement, search and rescue operations, and border security, showcasing their multifaceted importance across diverse applications. Video fraud detection is integral to multimedia security, where our task involves the precise identification of modified segments within video sequences. Current approaches to video fraud detection often rely on manual feature selection and models tailored to detect specific tampering types, such as copy-move or splicing. The general representation powers of deep learning models and the connection of multiple forensic characteristics are still not fully explored. This research uses a convolutional neural network (CNN) model to identify copy-move video forgeries. Copy-move forgery is a type of video tampering whereby a portion of the video is copied and pasted somewhere different in the same video to cover an essential video characteristic. The method that is being proposed involves dividing the video into individual frames, extracting features from each frame by using a CNN model that has already been trained, and then utilizing these features to train a new CNN model that would classify each frame as either legitimate or fabricated. The proposed method effectively detects copy-move video forgery with an exceptionally high accuracy rate, exceeding current methods of accuracy and computational effort. The proposed method outperformed all other approaches on the SULFA, GRIP, and VTD datasets. The model's accuracy was 85.42 %, 86.16 %, and 81.87 %, respectively, with the shortest times recorded being 9.6 sec, 11.4 sec, and 13.7 sec, respectively. Consequently, specialists can employ the suggested method as a machine-learning instrument for detecting fake videos in real-time.

Multi-Scale Feature Attention Fusion for Image Splicing Forgery Detection

Image splicing is a widely occurrence image tampering technology. With the rapid development of digital image processing technology, detecting image splicing forgery has become significantly challenging. Although various methods have been devised to identify such tampered images, existing approaches have not achieved optimal performance due to limitations in effectively leveraging feature maps of different scales. To address this issue, we propose a novel method for image splicing forgery detection called multi-scale feature attention fusion network (MFAF-Net). We propose a multi-scale atrous feature attention (MAFA) module designed to capture rich contextual features for multi-scale high-level feature fusion. Additionally, we present the multi-branch attention mechanism (MBAM) module to fuse contextual information from various branches for low-level features. This integration enhances the capability of low-level features to produce more refined pixel-level attention. We employ the weighted binary cross-entropy loss and dice loss in the MFAF-Net to overcome the imbalance between positive and negative samples. Extensive experiments demonstrate that the proposed MFAF-Net outperforms state-of-the-art methods. Robustness experiments also show our model exhibits image splicing forgery detection robustness under common attacks.

A Watermark-Based Scheme for Authenticating JPEG 2000 Image Integrity That Complies with JPEG Privacy and Security

Network development has made it easier to access multimedia material and to change it by allowing the collection, modification, and transmission of digital data. Additionally, this has led to a rise in malicious use, including unauthorized data distribution and copying. As the quantity of evil activities increases, security issues such as unauthorized use and image forgery are rising. While security solutions for JPEG-1 images are widely available, there remains a significant gap in protection for JPEG 2000 images. In this paper, we propose a new watermark-based forgery detection method to comply with the JPEG Privacy and Security standards and to authenticate JPEG 2000 image integrity in the discrete wavelet transform (DWT) domain. The method proposed divides JPEG 2000 images into groups of blocks (GOBs) within the DWT domain. The watermark is generated by collaborating with the neighboring GOBs and is embedded in the GOBs. It enables you to respond to the collage attack. Experimental results using various sample images show the superiority of the proposed method, showing negligible visual differences between the original and watermarked JPEG 2000 images.

MTFDN: An image copy‐move forgery detection method based on multi‐task learning

AbstractImage copy‐move forgery, where an image region is copied and pasted within the same image, is a simple yet widely employed manipulation. In this paper, we rethink copy‐move forgery detection from the perspective of multi‐task learning and summarize two characteristics of this problem: (1) Homology and (2) Manipulated traces. Consequently, we propose a multi‐task forgery detection network (MTFDN) for image copy‐move forgery localization and source/target distinguishment. The network consists of a hard‐parameter sharing feature extractor, global forged homology detection (GFHD) and local manipulated trace detection (LMTD) modules. The difference of feature distribution between the GFHD module and the LMTD module is significantly reduced by sharing parameters. Experimental results on several benchmark copy‐move forgery datasets demonstrate the effectiveness of our proposed MTFDN.

Hierarchical Progressive Image Forgery Detection and Localization Method Based on UNet

The rapid development of generative technologies has made the production of forged products easier, and AI-generated forged images are increasingly difficult to accurately detect, posing serious privacy risks and cognitive obstacles to individuals and society. Therefore, constructing an effective method that can accurately detect and locate forged regions has become an important task. This paper proposes a hierarchical and progressive forged image detection and localization method called HPUNet. This method assigns more reasonable hierarchical multi-level labels to the dataset as supervisory information at different levels, following cognitive laws. Secondly, multiple types of features are extracted from AI-generated images for detection and localization, and the detection and localization results are combined to enhance the task-relevant features. Subsequently, HPUNet expands the obtained image features into four different resolutions and performs detection and localization at different levels in a coarse-to-fine cognitive order. To address the limited feature field of view caused by inconsistent forgery sizes, we employ three sets of densely cross-connected hierarchical networks for sufficient interaction between feature images at different resolutions. Finally, a UNet network with a soft-threshold-constrained feature enhancement module is used to achieve detection and localization at different scales, and the reliance on a progressive mechanism establishes relationships between different branches. We use ACC and F1 as evaluation metrics, and extensive experiments on our method and the baseline methods demonstrate the effectiveness of our approach.

Exposing video surveillance object forgery by combining TSF features and attention-based deep neural networks

Recently, forensics has encountered a new challenge with video surveillance object forgery. This type of forgery combines the characteristics of popular video copy-move and splicing forgeries, failing most existing video forgery detection schemes. In response to this new forgery challenge, this paper proposes a Video Surveillance Object Forgery Detection (VSOFD) method including three parts components: (i) The proposed method presents a special-combined extraction technique that incorporates Temporal-Spatial-Frequent (TSF) perspectives for TSF feature extraction. Furthermore, TSF features can effectively represent video information and benefit from feature dimension reduction, improving computational efficiency. (ii) The proposed method introduces a universal, extensible attention-based Convolutional Neural Network (CNN) baseline for feature processing. This CNN processing architecture is compatible with various series and parallel feed-forward CNN structures, considering these structures as processing backbones. Therefore, the proposed CNN architecture benefits from various state-of-the-art structures, leading to addressing each independent TSF feature. (iii) The method adopts an encoder-attention-decoder RNN framework for feature classification. By incorporating temporal characteristics, the framework can further identify the correlations between the adjacent frames to classify the forgery frames better. Finally, experimental results show that the proposed network can achieve the best F1 = 94.69 % score, increasing at least 5–12 % from the existing State-Of-The-Art (SOTA) VSOFD schemes and other video forensics.

LBRT: Local-Information-Refined Transformer for Image Copy-Move Forgery Detection.

The current deep learning methods for copy-move forgery detection (CMFD) are mostly based on deep convolutional neural networks, which frequently discard a large amount of detail information throughout convolutional feature extraction and have poor long-range information extraction capabilities. The Transformer structure is adept at modeling global context information, but the patch-wise self-attention calculation still neglects the extraction of details in local regions that have been tampered with. A local-information-refined dual-branch network, LBRT (Local Branch Refinement Transformer), is designed in this study. It performs Transformer encoding on the global patches segmented from the image and local patches re-segmented from the global patches using a global modeling branch and a local refinement branch, respectively. The self-attention features from both branches are precisely fused, and the fused feature map is then up-sampled and decoded. Therefore, LBRT considers both global semantic information modeling and local detail information refinement. The experimental results show that LBRT outperforms several state-of-the-art CMFD methods on the USCISI dataset, CASIA CMFD dataset, and DEFACTO CMFD dataset.

Advancements in Video Forgery Detection: Novel Methods for Object and Facial Forgery Detection Using Temporal and Spatial Analysis

Advancements in Video Forgery Detection: Novel Methods for Object and Facial Forgery Detection Using Temporal and Spatial Analysis

Forgered Image Perception System using CNN Algorithms

The progression and diversification of methodologies in digital image forensics and manipulation detection are evident in existing work, presenting innovative techniques tailored to specific aspects of forgery detection. These methodologies encompass various challenges such as copy-move forgery, manipulation detection, demosaicing artifacts, image splicing, edge detection, and compression analysis. Together, these studies exemplify ongoing efforts to enhance the accuracy, efficiency, and reliability of forgery detection methods within the constantly evolving landscape of digital image manipulation. The proposed image forgery detection system represents a pioneering advancement, surpassing the limitations of conventional methodologies. It integrates a comprehensive array of detection techniques, including copy-move analysis, color filter array scrutiny, noise variance inconsistency detection, and double JPEG artifact identification. These techniques are bolstered by Convolutional Neural Networks (CNNs). This innovative fusion not only addresses the limitations inherent in existing systems but also signifies a significant leap forward in functionality and efficacy. By synergistically leveraging the strengths of each detection method within a CNN framework, our approach promises heightened accuracy, robustness, and adaptability in discerning even the most sophisticated forms of image tampering. Keywords— Image Tampering, Double JPEG Compression, Demosaicing Artifacts, Recursive Edge Detection, Copy-Move Forgery Detection.

Uncertainty-aware hierarchical labeling for face forgery detection

In this paper, we propose an uncertainty-aware hierarchical labeling method for face forgery detection, which aims to simultaneously explore the traces of forgery contents from a hierarchical level. Unlike existing face forgery detection methods that usually focus on local regions or entire images, our proposed approach takes advantage of hierarchical labeling as auxiliary supervised signals to capture hybrid information from pixel-level, patch-level and image-level. Moreover, we utilize Transformer architecture to extract the patch-level information to build a bridge between the pixel-level and the image-level information. However, the face forgery ground truth always carries data uncertainty due to the existence of the blending step and the compression process during face image manipulation. Thus we introduce an uncertainty learning method to formulate and leverage the data uncertainty. Extensive experimental results on five public datasets demonstrate that our approach not only achieves very competitive performance but also improves the generalization ability.

Method for image splicing forgery detection

Method for image splicing forgery detection

Synthetic images aid the recognition of human-made art forgeries.

Previous research has shown that Artificial Intelligence is capable of distinguishing between authentic paintings by a given artist and human-made forgeries with remarkable accuracy, provided sufficient training. However, with the limited amount of existing known forgeries, augmentation methods for forgery detection are highly desirable. In this work, we examine the potential of incorporating synthetic artworks into training datasets to enhance the performance of forgery detection. Our investigation focuses on paintings by Vincent van Gogh, for which we release the first dataset specialized for forgery detection. To reinforce our results, we conduct the same analyses on the artists Amedeo Modigliani and Raphael. We train a classifier to distinguish original artworks from forgeries. For this, we use human-made forgeries and imitations in the style of well-known artists and augment our training sets with images in a similar style generated by Stable Diffusion and StyleGAN. We find that the additional synthetic forgeries consistently improve the detection of human-made forgeries. In addition, we find that, in line with previous research, the inclusion of synthetic forgeries in the training also enables the detection of AI-generated forgeries, especially if created using a similar generator.

A Contemporary Survey on Deepfake Detection: Datasets, Algorithms, and Challenges

Deepfakes are notorious for their unethical and malicious applications to achieve economic, political, and social reputation goals. Recent years have seen widespread facial forgery, which does not require technical skills. Since the development of generative adversarial networks (GANs) and diffusion models (DMs), deepfake generation has been moving toward better quality. Therefore, it is necessary to find an effective method to detect fake media. This contemporary survey provides a comprehensive overview of several typical facial forgery detection methods proposed from 2019 to 2023. We also analyze and group them into four categories in terms of their feature extraction methods and network architectures: traditional convolutional neural network (CNN)-based detection, CNN backbone with semi-supervised detection, transformer-based detection, and biological signal detection. Furthermore, it summarizes several representative deepfake detection datasets with their advantages and disadvantages. Finally, we evaluate the performance of these detection models with respect to different datasets by comparing their evaluating metrics. Across all experimental results on these state-of-the-art detection models, we find that the accuracy is largely degraded if we utilize cross-dataset evaluation. These results will provide a reference for further research to develop more reliable detection algorithms.

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.

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.

Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in VIS and NIR Scenario

In recent years, with the rapid development of face editing and generation, more and more fake videos are circulating on social media, which has caused extreme public concerns. Existing face forgery detection methods based on frequency domain find that the GAN forged images have obvious grid-like visual artifacts in the frequency spectrum. But for synthesized videos, these methods only confine to a single frame and pay little attention to the most discriminative part and temporal frequency clue among different frames. To take full advantage of the rich information in video sequences, this paper performs video forgery detection on both spatial and temporal frequency domains and proposes a Discrete Cosine Transform-based Forgery Clue Augmentation Network (FCAN-DCT) to achieve a more comprehensive spectrum spatial-temporal feature representation. FCAN-DCT totally consists of a backbone network and two branches: Compact Feature Extraction (CFE) module and Frequency Temporal Attention (FTA) module. We conduct thorough experimental assessments on three visible light (VIS) based datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., FaceForensics++, Celeb-DF (v2), Wild-Deepfake), and our self-built video forgery dataset DeepfakeNIR, which is the first video forgery dataset on near-infrared (NIR) modality. The experimental results demonstrate the effectiveness and robustness of our method for detecting forgery videos in both VIS and NIR scenarios.DeepfakeNIR and code are available at https://github.com/AEP-WYK/DeepfakeNIR.

A generalized novel image forgery detection method using generative adversarial network

A generalized novel image forgery detection method using generative adversarial network

Frame Duplication Forgery Detection in Surveillance Video Sequences Using Textural Features

Frame duplication forgery is the most common inter-frame video forgery type to alter the contents of digital video sequences. It can be used for removing or duplicating some events within the same video sequences. Most of the existing frame duplication forgery detection methods fail to detect highly similar frames in the surveillance videos. In this paper, we propose a frame duplication forgery detection method based on textural feature analysis of video frames for digital video sequences. Firstly, we compute the single-level 2-D wavelet decomposition for each frame in the forged video sequences. Secondly, textural features of each frame are extracted using the Gray Level of the Co-Occurrence Matrix (GLCM). Four second-order statistical descriptors, Contrast, Correlation, Energy, and Homogeneity, are computed for the extracted textural features of GLCM. Furthermore, we calculate four statistical features from each frame (standard deviation, entropy, Root-Mean-Square RMS, and variance). Finally, the combination of GLCM’s parameters and the other statistical features are then used to detect and localize the duplicated frames in the video sequences using the correlation between features. Experimental results demonstrate that the proposed approach outperforms other state-of-the-art (SOTA) methods in terms of Precision, Recall, and F1Score rates. Furthermore, the use of statistical features combined with GLCM features improves the performance of frame duplication forgery detection.