Fake Images Research Articles

A EFFCIET DEEP FAKE FACE DETECTION USING DEEP INCEPTION NET LEARNING ALGORITHM

A Deep Fake Is Digital Manipulation Techniques That Use Deep Learning to Produce Deep Fake (Misleading) Images and Videos. Identifying Deep Fake Images Is the Most Difficult Part of Finding the Original. Due To the Increasing Reputation of Deep Fakes, Identifying Original Images and Videos Is More Crucial to Detect Manipulated Videos. This Paper Studies and Experiments with Different Methods That Can Be Used to Detect Fake and Real Images and Videos. The Convolutional Neural Network (Cnn) Algorithm Named Inception Net Has Been Used to Identify Deep Fakes. A Comparative Analysis Was Performed in This Work Based on Various Convolutional Networks. This Work Uses the Dataset from Kaggle With 401 Videos of Train Sample And 3745 Images Were Generated by Augmentation Process. The Results Were Evaluated with The Metrics Like Accuracy and Confusion Matrix. The Results of The Proposed Model Produces Better Results in Terms of Accuracy With 93% On Identifying Deep Fake Images and Videos.

Evaluating the Effectiveness of a Gan Fingerprint Removal Approach in Fooling Deepfake Face Detection

Deep neural networks are able to generate stunningly realistic images, making it easy to fool both technology and humans into distinguishing real images from fake ones. Generative Adversarial Networks (GANs) play a significant role in these successes (GANs). Various studies have shown that combining features from different domains can produce effective results. However, the challenges lie in detecting these fake images, especially when modifications or removal of GAN components are involved. In this research, we analyse the high-frequency Fourier modes of real and deep network-generated images and show that Images generated by deep networks share an observable, systematic shortcoming when it comes to reproducing their high-frequency features. We illustrate how eliminating the GAN fingerprint in modified pictures' frequency and spatial spectrum might affect deep-fake detection approaches. In-depth review of the latest research on the GAN-Based Artifacts Detection Method. We empirically assess our approach to the CNN detection model using style GAN structures 140k datasets of Real and Fake Faces. Our method has dramatically reduced the detection rate of fake images by 50%. In our study, we found that adversaries are able to remove the fingerprints of GANs, making it difficult to detect the generated images. This result confirms the lack of robustness of current algorithms and the need for further research in this area.

Diversified realistic face image generation GAN for human subjects in multimedia content creation

AbstractFace image generation plays an important role in generating innovative and unique multimedia content using the GAN model. With these qualities of the GAN model, they have numerous challenges in the human face image generation. The problems encountered in the generation of facial images are like blurriness in images, incomplete details in the generated facial images, high computational power requirements, and so forth. In this manuscript, we proposed a GAN model that utilizes the composite strength of VGG‐16 and ResNet‐50's models to overcome those difficulties. It uses VGG‐16 to build a discriminator model to discriminate between real and fake images. The generator model utilizes a combination of components from the ResNet‐50 and VGG‐16 models to enhance the image generation process at each iteration, resulting in the creation of realistic face images. The proposed DRFI GAN (Diversified and Realistic Face Image Generation GAN) model's generator achieves an impressive low FID score of 20.50, which is less than existing state‐of‐the‐art approaches. Furthermore, our findings indicate that the images generated by the DRFI GAN model exhibit 10%–15% greater efficiency and realism with reduced training time compared to existing state‐of‐the‐art methods with lower FID scores.

A YOLOV3-Based Method for Detecting Deepfake Manipulated Facial Images

With the advancement of technology and the development of applications that make it easier to transfer images, sounds and videos to the virtual environment, it has become much easier to access people's personal information, videos and images. Deepfake technology produces fakes of authentic images or sounds using deep learning and artificial intelligence techniques. Today, in addition to being used in the entertainment and film industries, it is also used in situations such as creating fake news and discrediting people. Different studies have been conducted in the literature to detect deepfake images and videos to prevent these situations. In this study, a comprehensive literature review was conducted. Real and fake images were collected and labelled from different datasets or videos, and a dataset was created by applying the necessary pre-processing steps. With the created dataset, training was carried out with YOLOv3 technology, which calculates class probabilities differently from traditional methods using Convolutional Neural Networks (CNN) and handles all operations in a single regression problem, which can make fast and high-accurate detection, and the modelling process is explained. With the tests performed in the study, the model that can detect fake images produced with deepfake technology with 95% accuracy was obtained.

The recognition and analysis of fake images based on deep neural network

The generation of fake images and the process of technology is developing quickly, which has generated great concern about its impact on society. To effectively identify the fake image, this article introduces a deep learning-based identification method. This article uses a convolutional neural network (CNN) neural network to construct a model for machine learning image recognition and lets it learn a dataset of hundreds of real and fake pictures to let it complete tasks that are difficult for humans to complete. the use of deep learning to solve this problem has the following advantages: the results can be observed after data input, which is conservative and fast, and there is no need to manually design rules as Deep learning methods can optimise the loss function to learn the rules, mining potential features of data, with strong representation ability. Using this method, the accuracy result is over 60%. This paper proves a machine can learn and recognise real and fake pictures, which is specifically inspiring that learning-based methods can also solve the challenging problem of images.

CSC-Net: Cross-Color Spatial Co-Occurrence Matrix Network for Detecting Synthesized Fake Images

Recently, the Generative Adversarial Networks (GAN) generated images have been spread over the social networks, which brings the new challenge in the the community of media forensics. Although some reliable forensic tools have advanced the study of detecting GAN generated images, while the detection accuracy cannot be guaranteed when facing the malicious post-processing attacks, especially in the practical social network scenario. Thus, in this context, we propose a novel well-designed deep neural network equipped with handed-crafted features for dealing with this problem. In particular, relying on the Cross-color Spatial Co-occurrence Matrix (CSCM), the discriminative features are extracted after carefully analyzing and selecting the most effective color channels. Next, the fused features are fed into the deep neutral network for training a high-efficient forensic detector. Extensive experimental results empirically verify that in most detection scenarios, our proposed detector performs superiorly to the prior arts, especially in the case of post-processing attacks. Moreover, we also highlight the relevance of the proposed detector over the realistic social network platforms, and its generalization capability in three different scenarios.

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.

Deepfake image detection and classification model using Bayesian deep learning with coronavirus herd immunity optimizer

<p>Deepfake images are combined media constructed from deep learning (DL) methods, usually Generative Adversarial Networks (GANs), to manipulate visual content, often giving rise to convincing and fabricating descriptions of scenes or people. The Bayesian machine learning (ML) model has made crucial strides over the past two decades, illustrating promise in diverse applications. In deepfake images, detection utilizes computer vision (CV) and ML to spot manipulated content by analyzing unique artefacts and patterns. Recent techniques utilize DL to train neural networks to discriminate between real and fake images, improving the fight against digital manipulation and preserving media integrity. These systems can efficiently detect subtle inconsistencies or anomalies specific to deepfake creations by learning from large datasets of both real and deepfake images. This enables the mitigation of fraudulent content and reliable detection in digital media. We introduce a new Coronavirus Herd Immunity Optimizer with a Deep Learning-based Deepfake Image Detection and Classification (CHIODL-DIDC) technique. The CHIODL-DIDC technique aimed to detect and classify the existence of fake images. To accomplish this, the CHIODL-DIDC technique initially used a median filtering (MF) based image filtering approach. Besides, the CHIODL-DIDC technique utilized the MobileNetv2 model for extracting feature vectors. Moreover, the hyperparameter tuning of the MobileNetv2 model was accomplished using the CHIO method. For deepfake image detection, the CHIODL-DIDC technique implements the deep belief network (DBN) model. Finally, the Bayesian optimization algorithm (BOA) was utilized to select the effectual hyperparameter of the DBN model. The CHIODL-DIDC method's empirical analysis was examined using a benchmark fake image dataset. The performance validation of the CHIODL-DIDC technique illustrated a superior accuracy value of 98.16% over other models under $ Acc{u}_{y} $ , $ Pre{c}_{n} $ , $ Rec{a}_{l} $ , $ {F}_{Score} $ , and MCC metrics.</p>

CNN-GMM approach to identifying data distribution shifts in forgeries caused by noise: a step towards resolving the deepfake problem.

Recently, there have been notable advancements in video editing software. These advancements have allowed novices or those without access to advanced computer technology to generate videos that are visually indistinguishable to the human eye from real ones to the human observer. Therefore, the application of deepfake technology has the potential to expand the scope of identity theft, which poses a significant risk and a formidable challenge to global security. The development of an effective approach for detecting fake videos is necessary. Here, we introduce a novel methodology that employs a convolutional neural network (CNN) and Gaussian mixture model (GMM) to effectively differentiate between fake and real images or videos. The proposed methodology presents a novel CNN-GMM architecture in which the fully connected (FC) layer in the CNN is replaced with a customized Gaussian mixture model (GMM) fully connected layer. The GMM layer utilizes a weighted set of Gaussian probability density functions (PDFs) to represent the distribution of data frequencies in both real and fake images. This representation indicates there is a shift in the distribution of the manipulated images due to added noise. The CNN-GMM model demonstrates the ability to accurately identify variations resulting from different types of deepfakes within the probability distribution. It achieves a high level of classification accuracy, reaching up to 100% in training accuracy and up to 96% in validation accuracy. Notwithstanding the ratio of the genuine class to the counterfeit class being 16.6% to 83.4%, the CNN-GMM model exhibited high-performance metrics in terms of recall, accuracy, and F-score when classifying the least genuine class.

MMGANGuard: A Robust Approach for Detecting Fake Images Generated by GANs Using Multi-Model Techniques

MMGANGuard: A Robust Approach for Detecting Fake Images Generated by GANs Using Multi-Model Techniques

Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

Sustainable Artificial Intelligence: Assessing Performance in Detecting Fake Images

Sustainable Artificial Intelligence: Assessing Performance in Detecting Fake Images

Improving synthetic media generation and detection using generative adversarial networks.

Synthetic images ar---e created using computer graphics modeling and artificial intelligence techniques, referred to as deepfakes. They modify human features by using generative models and deep learning algorithms, posing risks violations of social media regulations and spread false information. To address these concerns, the study proposed an improved generative adversarial network (GAN) model which improves accuracy while differentiating between real and fake images focusing on data augmentation and label smoothing strategies for GAN training. The study utilizes a dataset containing human faces and employs DCGAN (deep convolutional generative adversarial network) as the base model. In comparison with the traditional GANs, the proposed GAN outperform in terms of frequently used metrics i.e., Fréchet Inception Distance (FID) and accuracy. The model effectiveness is demonstrated through evaluation on the Flickr-Faces Nvidia dataset and Fakefaces d--ataset, achieving an FID score of 55.67, an accuracy of 98.82%, and an F1-score of 0.99 in detection. This study optimizes the model parameters to achieve optimal parameter settings. This study fine-tune the model parameters to reach optimal settings, thereby reducing risks in synthetic image generation. The article introduces an effective framework for both image manipulation and detection.

Fake Image Detection: A Comprehensive Review

Fake Image Detection: A Comprehensive Review

Neural Network-Based Algorithm for Identification of Recaptured Images

With the improvement of digital image display technology, the “secondary imaging” caused by digital cameras is also gradually popularized, and the quality of the recaptured image formed by this imaging is also getting higher and higher, and this kind of high-quality fake image has caused great threat to digital images security. We propose a neural network-based recaptured image identification algorithm and use the difference between two types of images to build the identification algorithm in the frequency domain. The algorithm uses filtering to obtain the feature images which are the high-frequency and low-frequency filtering images, in order to further distinguish the image differences, the direction of the filtered image obtained from high-frequency images, each direction of the filtered image contains high-frequency information at different angles, and the low-frequency image is downsampled. At the same time, the low-frequency image is downsampled to obtain a multi-scale filtered image. The algorithm extracts the features from previous images as the feature values for classification, and finally uses neural networks for classification to obtain the classification results, and these prove that the algorithm presented is able to differentiate the recaptured images effectively in this paper.

Enhanced Adversarial Attack for Avoidance of Fake Image Detection

Enhanced Adversarial Attack for Avoidance of Fake Image Detection

Generative adversarial network: a statistical-based deep learning paradigm to improve detecting breast cancer in thermograms.

Thermography, as a harmless modality, thanks to its low equipment complexity in parallel with quick and cheap access, has been able to come up as a method with significant potential in the diagnosis of some cancers in recent years. However, the complexity of the images resulting from this method has caused the use of deep learning to interpret thermograms. A limiting factor in this process is the strong dependence of deep learning methods on the number of training data, which is a serious challenge in thermography due to the young age of this technology and the lack of available images. In this paper, an attempt is made to reduce the above challenge by utilizing the concept of statistical learning in such a way that the statistical distribution of the original data is estimated by using generative adversarial networks (i.e., GAN). Then, several fake images are reconstructed based on the estimated distribution in order to increase the training thermograms. Since the fake images are reconstructed based on similar statistics of real thermograms in each class, the effective features of each class are preserved to a significant extent in the reconstruction process. The use of this method indicates a significant improvement in the separation of healthy and cancerous thermograms compared to the benchmark method which does not use the concept of GAN in such a way that characteristics of sensitivity and accuracy are improved in ranges of 3-9% and 3-7%, respectively. In terms of specificity, although we have seen an improvement of up to 9%, in some cases, small drops of up to 2% have also been observed, which can still be justified due to the significant improvement in sensitivity and accuracy.

Semi-supervised generative adversarial networks for improved colorectal polyp classification using histopathological images

Early and accurate detection of dysplasia in colorectal polyps can improve prognosis and increase survival chances. Recently, automated learning-based approaches using histopathological images have been adopted for improved classification of polyps. The supervised learning approaches do not provide a reliable classification performance due to limited annotated samples. But, in unsupervised learning, some hidden features are extracted from the unlabeled data which may not be effective in discriminating the complex patterns of the dataset. A generative adversarial network (GAN) is proposed in this work based on a semi-supervised framework for colorectal polyp classification using histopathological images. Our framework learns the discriminating features in an adversarial manner from the limited labeled and huge unlabeled data. In the supervised mode, the discriminator of the proposed model is trained to classify the real histopathological images, whereas, in the unsupervised mode, it tries to discriminate between real and fake images, similar to the classical GAN network. By training in unsupervised mode, the discriminator can identify and extract the subtle features from unlabeled images, to develop a generalized robust model. Our technique yielded classification accuracies of 87.50% and 76.25% using 25% and 50% majority voting schemes, respectively, on the UniToPatho dataset.

3D-FGAN: A 3D stochastic reconstruction method of digital cores

Digital core reconstruction can quantitatively characterize the interior microstructure of porous media and revealing variations in the petrophysical properties of rocks, which can be classified into physical experimental methods and numerical reconstruction methods. Numerical reconstruction methods utilize the information contained in 2D or 3D images to reconstruct 3D digital cores, which is relatively inexpensive and more efficient, but traditional numerical methods usually cannot reuse extracted statistical information from previous reconstruction process and may generate unsatisfying results. Nowadays, the development of deep learning has opened a door to the high-quality reconstruction of digital cores. Generative adversarial networks (GANs), viewed as an important branch of deep learning, have been used for the 3D digital core reconstruction. GANs have a strong feature extraction capability and can generate fake images that closely resemble the real ones. Nevertheless, due to the complex structure of 3D core images, especially those with high anisotropy, GANs and their variants still have difficulties in generating satisfying pore structure. To address the above issues, we propose a variant of GANs called 3D fast generative adversarial network (3D-FGAN) to rapidly generate high-quality 3D digital core images. Several channel attention modules called skip-layer excitation blocks are added to the generator of 3D-FGAN, enhancing the utilization of important features. Auto-encoders are employed to optimize the discriminator of 3D-FGAN, which makes the discriminator more comprehensively extract the features of input images and thus better guides the generator. Through the evaluation metrics of pore space distribution (e.g., isolated pore space and connected pore space, pore diameters and throat diameters), multiple-point connectivity, absolute permeability and diversity test, the quality of 3D-FGAN can be proved. Meanwhile, in the reconstruction of anisotropic shales with evident fractures, 3D-FGAN can quickly produce higher-quality images compared with some other numerical reconstruction methods.

Sketch to Image Translation using Generative Adversarial Network

Using a Generative Adversarial Network (GAN) has proven its ability to successfully implement realistic images in image translation fields. It has its successful implementation in the sketch-to-image translation, too. Generative adversarial networks are widely used for the purpose of image translation. Most discriminators in generative adversarial networks use encoder or decoder blocks for image segmentation and classification tasks. U-net-based architecture is mostly used in the generator but rarely in the discriminator. If used in the discriminator, it is used for image resolution increment and segmentation tasks. In this research, a U-net-based discriminator is used for image translation tasks. U-net-based discriminator uses local and global differences between the real and fake images, which helps maintain global and local data representation. Resnet-9, used in the generator, uses skip connections, shortcuts, and concatenations, enabling information to flow from earlier to later layers. This helps preserve the original image features and solves the vanishing gradient problems in normal generators. The use of a strong discriminator and effective generator helps in the improvement system's performance. The available dataset was unpaired at the same time. Datasets from various sources were combined and formed a sketch-image pair. The input is a 512x256 human sketch and a corresponding real image pair. The image pair is split into sketch and image with dimensions 256x256. The system's output is the human face image of the corresponding sketch.