Fake Image Detection Research Articles

Generated or Not Generated (GNG): The Importance of Background in the Detection of Fake Images

Facial biometrics are widely used to reliably and conveniently recognize people in photos, in videos, or from real-time webcam streams. It is therefore of fundamental importance to detect synthetic faces in images in order to reduce the vulnerability of biometrics-based security systems. Furthermore, manipulated images of faces can be intentionally shared on social media to spread fake news related to the targeted individual. This paper shows how fake face recognition models may mainly rely on the information contained in the background when dealing with generated faces, thus reducing their effectiveness. Specifically, a classifier is trained to separate fake images from real ones, using their representation in a latent space. Subsequently, the faces are segmented and the background removed, and the detection procedure is performed again, observing a significant drop in classification accuracy. Finally, an explainability tool (SHAP) is used to highlight the salient areas of the image, showing that the background and face contours crucially influence the classifier decision.

Fake Social Media Post Detection by Using Deep Learning Algorithm

In today's digital age, social media serves as both a boon and a bane, offering immense opportunities for personal growth while also being a breeding ground for misinformation and deception. The rise of sophisticated multimedia manipulation techniques has blurred the line between reality and fiction, presenting significant challenges to discerning genuine content from fabricated ones. This paper presents an overview of the escalating threat posed by fake posts on social media and proposes a novel approach for their detection. The proposed method leverages advanced technologies such as Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs) to distinguish between authentic and manipulated posts. Unlike traditional approaches reliant on manual feature extraction, our framework employs deep learning methodologies for more nuanced pattern recognition and generalization to unseen data. By combining ELA for initial image analysis and CNNs for deep feature extraction, followed by classification using Support Vector Machines (SVMs) and K-Nearest Neighbors (KNNs), we achieve robust detection of fake images .Experimental results demonstrate the effectiveness of our approach, with a peak accuracy of 91.3% using Residual Networks and KNN. This method offers a promising solution to mitigate the proliferation of fake images on social media platforms, thereby combating the spread of misinformation, propaganda, and chaos.

Investigating the Evolving Landscape of Deepfake Technology: Generative AI's Role in it's Generation and Detection

The world of artificial intelligence is constantly changing, with Generative AI and Large Language Models (LLMs) leading the way in bringing new technological advancements. This paper offers a detailed look at these groundbreaking technologies and how they are shaping the digital world today. We explore the technical aspects of Generative AI and LLMs, explain their unique features, and compare them to traditional AI models.One of the key focuses of our research is the growing issue of DeepFakes—artificial intelligence-generated media that presents a significant challenge in verifying content. We conduct a thorough examination of few deepfake detection techniques out of which we will be implementing and analyzing one of them. Our research implements a framework for Deep Fake Image Detection. The suggested solution utilizes a RESNET-50(Residual Network with 50 layers) and MTCNN (Multi-task Cascaded Convolutional Networks) models for detecting whether the images are real or fake. This study conducts the Hypothesis testing for the proposed solution taking in consideration that the current Deepfake detection algorithms are less effective in detecting highly realistic Deepfakes compared to less sophisticated manipulations. By investigating the convergence of deep learning, neural networks, and sophisticated algorithms, we set the stage for advancements in AI-based content verification.

Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images

Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. While these models have numerous benefits across various sectors, they have also raised concerns about the potential misuse of fake images and cast new pressures on fake image detection. In this work, we pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models. Firstly, we conduct a comprehensive analysis of the performance of contrastive and classification-based visual features, respectively extracted from CLIP-based models and ResNet or ViT-based architectures trained on image classification datasets. Our results demonstrate that fake images share common low-level cues, which render them easily recognizable. Further, we devise a multimodal setting wherein fake images are synthesized by different textual captions, which are used as seeds for a generator. Under this setting, we quantify the performance of fake detection strategies and introduce a contrastive-based disentangling method that lets us analyze the role of the semantics of textual descriptions and low-level perceptual cues. Finally, we release a new dataset, called COCOFake, containing about 1.2M images generated from the original COCO image-caption pairs using two recent text-to-image diffusion models, namely Stable Diffusion v1.4 and v2.0.

Generalized and robust model for GAN-generated image detection

Generative adversarial network (GAN)-generated image detection is a crucial yet challenging problem. The advancement of diverse generative models makes it simpler to build realistic synthetic images, but it also increases the likelihood of using images maliciously. Recent techniques of fake image detection often overfit to specific GANs, incapable of generalizing to other GANs. Each GAN leaves its distinct fingerprint on the images it generates. We propose a simple and compact attention network that exploits high-frequency and noise residual patterns of images. We design two-channel image representations in the preprocessing phase, with one channel using a bilateral high pass filter (BiHPF) for high-frequency patterns and the other using photo response non-uniformity (PRNU) for noise residuals capturing from GAN-generated and real images. The feature maps pass through the attention network with a dense layer for classification tasks. We assess the generalizability and robustness of the model through extensive experiments. The model is evaluated on a benchmark dataset in various settings, including cross-category, cross-GAN models with varying class settings and different color manipulations. This study demonstrates that the proposed model outperforms state-of-the-art (SOTA) methods by approximately 5% in different experiment settings.

Enhancing Fake Image Detection: A Novel Two-Step Approach Combining GANs and CNNs

The proliferation of fake images in today's digital landscape poses a significant threat to various domains, including media integrity, social media, and online security. Recognizing the urgent need to distinguish real images from their deceptive counterparts, this paper underscores the importance of developing a robust detection system. While substantial efforts have been made in the realms of computer vision and deep learning, the advent of Generative Adversarial Networks (GANs) has added a new layer of complexity to this challenge. In response to these evolving threats, we present a novel two-step methodology for detecting fake images, with a specific focus on those generated by GANs. Our approach harnesses the combined strengths of GANs and traditional Convolutional Neural Networks (CNNs), offering a comprehensive solution that significantly enhances accuracy in identifying both fake images and machine-generated fake images. The results of our experiments demonstrate the efficacy of our methodology. Using CNNs alone, we achieved a training accuracy of 87%. However, when employing the collaborative power of GANs and CNNs, our model exhibited a remarkable accuracy rate of 94.4%. This substantial improvement underscores the superiority of the GANs+CNN approach, suggesting its potential as a groundbreaking solution in the realm of fake image detection. This research opens up new horizons in fields such as media forensics, social media monitoring, and online security, where the ability to discern genuine content from manipulated or synthetic media is of paramount importance. The promising outcomes of this study not only provide an immediate and effective solution but also pave the way for further exploration and innovation in this critical area of digital security.

Fake Image Detection: A Comprehensive Review

Fake Image Detection: A Comprehensive Review

Enhanced Adversarial Attack for Avoidance of Fake Image Detection

Enhanced Adversarial Attack for Avoidance of Fake Image Detection

A Survey of Detection and Mitigation for Fake Images on Social Media Platforms

Recently, the spread of fake images on social media platforms has become a significant concern for individuals, organizations, and governments. These images are often created using sophisticated techniques to spread misinformation, influence public opinion, and threaten national security. This paper begins by defining fake images and their potential impact on society, including the spread of misinformation and the erosion of trust in digital media. This paper also examines the different types of fake images and their challenges for detection. We then review the recent approaches proposed for detecting fake images, including digital forensics, machine learning, and deep learning. These approaches are evaluated in terms of their strengths and limitations, highlighting the need for further research. This paper also highlights the need for multimodal approaches that combine multiple sources of information, such as text, images, and videos. Furthermore, we present an overview of existing datasets, evaluation metrics, and benchmarking tools for fake image detection. This paper concludes by discussing future directions for fake image detection research, such as developing more robust and explainable methods, cross-modal fake detection, and the integration of social context. It also emphasizes the need for interdisciplinary research that combines computer science, digital forensics, and cognitive psychology experts to tackle the complex problem of fake images. This survey paper will be a valuable resource for researchers and practitioners working on fake image detection on social media platforms.

Verity Vision: A Comprehensive Fake Image Detection Tool for Deepfakes and Manipulated Images

Verity Vision: A Comprehensive Fake Image Detection Tool for Deepfakes and Manipulated Images

An evolutionary- convolutional neural network for fake image detection

<p><span lang="EN-US">The fast development in deep learning techniques, besides the wide spread of social networks, facilitated fabricating and distributing images and videos without prior knowledge. This paper developed an evolutionary learning algorithm to automatically design a convolutional neural network (CNN) architecture for deepfake detection. Genetic algorithm (GA) based on residual network (ResNet) and densely connected convolutional network (DenseNet) as building block units for feature extraction versus multilayer perceptron (MLP), random forest (RF) and support vector machine (SVM) as classifiers generates different CNN structures. A local search mutation operation proposed to optimize three layers: (batch normlization, activation function, and regularizes). This method has the advantage of working on different datasets without preprocessing. Findings using two datasets evidence the efficiency of the suggested approach where the generated models outperform the state-of-art by increasing 1% in the accuracy; this confirms that intuitive design is the new direction for better generalization.</span></p>

Survey of Fake Image Synthesis and its Detection

In recent times, image synthesis has attracted significant attention of people for both positive and negative reasons. Images can be easily synthesized using various techniques. This paper surveys various techniques for image synthesis as well as its detection in a unique structured manner, to enable a perspective on this iterative phenomenon. The paper describes both advantages and limitations starting from simple fake image detection to AI synthesized image detection approaches that are available in the literature. Generative Adversarial Network (GAN) is the trending algorithm for artificial image synthesis, because the faces generated by GAN are highly realistic. As discriminators are already present in the GAN’s structure, any attempt to create a distinguisher that detects fake images synthesized by GAN, needs to structure itself to detect all existing patterns of fake image synthesis including that of GAN.

New Finding and Unified Framework for Fake Image Detection

Recently, fake face images generated by generative adversarial network (GAN) have been widely spread in social networks, raising serious social concerns and security risks. To identify the fake images, the top priority is to find what properties make the fake images different from the real images. In this letter, we reveal an important observation about real/fake images, i.e., the GAN generated fake images contain stronger non-local self-similarity than the real images. Motivated by this observation, we propose a simple yet effective non-local attention based fake image detection network, namely NAFID, to distinguish GAN generated fake images from real images. Specifically, we develop a non-local feature extraction (NFE) module to extract the non-local features of the real/fake images, followed by a multi-stage classification module to distinguish the images with the extracted non-local features. Experimental results on various datasets demonstrate the superiority of our NAFID over state-of-the-art (SOTA) face forgery detection methods. More importantly, since the NFE module is independent from classification, we can plug it into any other forgery detection models. The results show that the NFE module can consistently improve the detection accuracy of other models, which verifies the universality of the proposed method. The software code is available through the link <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/BiheZhao/NAFID</uri> .

Information-Containing Adversarial Perturbation for Combating Facial Manipulation Systems

With the development of deep learning technology, the facial manipulation system has become powerful and easy to use. Such systems can modify the attributes of the given facial images, such as hair color, gender, and age. Malicious applications of such systems pose a serious threat to individuals’ privacy and reputation. Existing studies have proposed various approaches to protect images against facial manipulations. Passive defense methods aim to detect whether the face is real or fake, which works for posterior forensics but can not prevent malicious manipulation. Initiative defense methods protect images upfront by injecting adversarial perturbations into images to disrupt facial manipulation systems but can not identify whether the image is fake. To address the limitation of existing methods, we propose a novel two-tier protection method named Information-containing Adversarial Perturbation (IAP), which provides more comprehensive protection for facial images. We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection. Recovering the message in adversarial examples with a decoder serves passive protection, contributing to provenance tracking and fake image detection. We introduce a feature-level correlation measurement that is more suitable to measure the difference between the facial images than the commonly used mean squared error. Moreover, we propose a spectral diffusion method to spread messages to different frequency channels, thereby improving the robustness of the message against facial manipulation. Extensive experimental results demonstrate that our proposed IAP can recover the messages from the adversarial examples with high average accuracy and effectively disrupt the facial manipulation systems.

Generalized Fake Image Detection Method Based on Gated Hierarchical Multi-Task Learning

Generalized Fake Image Detection Method Based on Gated Hierarchical Multi-Task Learning

An improvised CNN model for fake image detection

An improvised CNN model for fake image detection

Fake face image detection using feature network

In the recent times, the image data in social networks such as Instagram, Whatsapp, Facebook, Snapchat, twitter etc has an exponential growth in terms of volume, variety due to the velocity of the data stream. On the other hand, the advancements in the image and video processing led to increase in the fake images relatively in huge volumes. Due to the involvement in spreading fake news and leading mob incitements fake images became major concern to handle that demands an efficient a fake image detector is of at most concern to entire social networking organizations. In this paper, a deep learning framework is proposed that differentiates fabricated parts of the image from the real image using supervised learning strategies. Also a modified neural network structure called the Fake Feature Network is proposed in this work which consists of advanced convolution networks. In order to make model effective, the proposed methodology has a two major steps in learning which combines a modified neural structure that uses classifier and pairwise learning for the fake image detection.

FakeLocator: Robust Localization of GAN-Based Face Manipulations

Full face synthesis and partial face manipulation by virtue of the generative adversarial networks (GANs) and its variants have raised wide public concerns. In the multi-media forensics area, detecting and ultimately locating the image forgery has become an imperative task. In this work, we investigate the architecture of existing GAN-based face manipulation methods and observe that the imperfection of upsampling methods therewithin could be served as an important asset for GAN-synthesized fake image detection and forgery localization. Based on this basic observation, we have proposed a novel approach, termed <i>FakeLocator</i>, to obtain high localization accuracy, at full resolution, on manipulated facial images. To the best of our knowledge, this is the very first attempt to solve the GAN-based fake localization problem with a gray-scale fakeness map that preserves more information of fake regions. To improve the universality of <i>FakeLocator</i> across multifarious facial attributes, we introduce an attention mechanism to guide the training of the model. To improve the universality of <i>FakeLocator</i> across different DeepFake methods, we propose partial data augmentation and single sample clustering on the training images. Experimental results on popular FaceForensics&#x002B;&#x002B;, DFFD datasets and seven different state-of-the-art GAN-based face generation methods have shown the effectiveness of our method. Compared with the baselines, our method performs better on various metrics. Moreover, the proposed method is robust against various real-world facial image degradations such as JPEG compression, low-resolution, noise, and blur.

SiteForge: Detecting and localizing forged images on microblogging platforms using deep convolutional neural network

Microblogging applications are currently used to disseminate information with concise text and images. Nevertheless, they are also the largest platform for circulating forged images. Forged images are digital photographs that have been modified to deceive or distort the information they communicate. These manipulated images posted on microblogging apps like Twitter often create biased user emotions leading to harmful consequences like religious feuds or riots. Microblogging platforms and fact-checking industry are investing in artificial intelligence solutions to detect these forged images on time. Many image forensic techniques are proposed earlier, but their effectiveness falls short on real-world images shared over microblogging sites. As forged images shared over these platforms are typically altered using multiple manipulation techniques, it is hard for forensic techniques to detect them. This paper proposes a customized convolutional neural network with an attention mechanism to spot fake images shared over microblogging platforms. Deep learning convolutional networks learn the intrinsic feature set of images and can detect the forged images. To handle multiple manipulations in an image, the applied attention mechanism focuses on the most relevant image region to learn the inherent feature sets. The model utilizes High-pass filters from the image processing domain to initialize kernel weights of the neural network. This supports the proposed model to converge faster and achieve better accuracy. The pooling layers are designed to specifically handle images from microblogging sites. The solution is universal and can detect complex tampering scenarios like text-editing, face-swapping, copy-move, splicing and mirroring. Local Interpretable Model-agnostic Explanations (LIME) is utilized to localize the manipulated region in a forged image. LIME also adds interpretability and confidence to the proposed model, a common concern in deep learning models. The model is verified against the publicly available CASIA 2.0 dataset. An accuracy score of 94.7% is achieved, which is better than the previous state-of-art papers in fake image detection. In order to test the model on real-world images published on Twitter, a recent dataset is built from an Indian viewpoint. The model achieves a modest accuracy of 83.2% over the real-world Twitter dataset. The experiment proves that the proposed model can accurately detect the forged images over social platforms. It can be utilized in the fact-checking field to improve manual efforts. It will also support manual fact-checkers in swift decision making.

Enhanced model for fake image detection (EMFID) using convolutional neural networks with histogram and wavelet based feature extractions

In recent scenario of digital world and image forensics image is forged or unforged to be checked. It is a critical process in the areas where the images are taken as crucial proof in decision making in several cases. Hence, for handling the delinquent activities on image, a novel model, which is capable of detecting or classifying the authenticated and altered images, is required. A new model called Enhanced Model for Fake Image Detection (EMFID) is used for classifying the obtained images as AUTHENTIC and FORGED. Moreover, this work also discusses about the images that are altered with the splicing methods that is performed by cropping and pasting certain image part from one to another original image. The proposed model comprises four phases such as Image Pre-processing, Histogram based Feature Extraction, Discrete Wavelet Transform based Feature Extraction, Image Classification with ConvolutionalNeural Networks (CNN). The test images that are obtained from data set are classified under Authentic Class and Forged class. The results are better with classification accuracy and model efficiency.