Hierarchical Learning and Dummy Triplet Loss for Efficient Deepfake Detection

In recent years, the multimedia forensics and security community has seen remarkable progress in multitask learning for DeepFake (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i>, face forgery) detection. The prevailing approach has been to frame DeepFake detection as a binary classification problem augmented by manipulation-oriented auxiliary tasks. This scheme focuses on learning features specific to face manipulations with limited generalizability. In this paper, we delve deeper into semantics-oriented multitask learning for DeepFake detection, capturing the relationships among face semantics via joint embedding. We first propose an automated dataset expansion technique that broadens current face forgery datasets to support semantics-oriented DeepFake detection tasks at both the global face attribute and local face region levels. Furthermore, we resort to the joint embedding of face images and labels (depicted by text descriptions) for prediction. This approach eliminates the need for manually setting task-agnostic and task-specific parameters, which is typically required when predicting multiple labels directly from images. In addition, we employ bi-level optimization to dynamically balance the fidelity loss weightings of various tasks, making the training process fully automated. Extensive experiments on six DeepFake datasets show that our method improves the generalizability of DeepFake detection and renders some degree of model interpretation by providing human-understandable explanations.

We present a learning-based method for detecting and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the two audio and visual modalities from within the same video. Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is real or fake. We propose a deep learning network, inspired by the Siamese network architecture and the triplet loss. To validate our model, we report the AUC metric on two large-scale deepfake detection datasets, DeepFake-TIMIT Dataset and DFDC. We compare our approach with several SOTA deepfake detection methods and report per-video AUC of 84.4% on the DFDC and 96.6% on the DF-TIMIT datasets, respectively. To the best of our knowledge, ours is the first approach that simultaneously exploits audio and video modalities and also perceived emotions from the two modalities for deepfake detection.

Despite the promising advances in deepfake detection on current datasets, detecting visual deepfakes in real-world scenarios (e.g., deepfake videos and live streaming on YouTube) remains a challenge due to the inherent quality degradation such as unpredictable compression employed by social media platforms. Such degradation perturbs discernible forgery clues and diminishes the effectiveness of deepfake detection methods, raising a critical safety concern to the misuse of forgery faces in real-world scenarios. In this paper, we aim to understand the impacts of real-world degradation on the robustness of deepfake detection. Particularly, we investigate the risk of degraded deepfakes towards their detection on two real-world scenarios (i.e., deepfake videos and deepfake live streaming on social media platforms). By measuring the effects of real-world degradations on the performance and representation capabilities of detection models, we reveal that real-world deepfakes can be simulated via common degradation operations (e.g., JPEG compression) as they are perceptually similar to deepfake detectors. By analyzing the training dynamics under different sequences of training samples, we observe that the training order of deepfakes progressing from non-degraded (easy) to heavily degraded (hard) enhances the adaptability of detection models to various degradation in real-world scenarios. Drawing from these observations, we present a novel deepfake detection method ProFake to enhance the robustness of deepfake detection against real-world quality degradations. ProFake enables quality-adaptive learning via progressively degrade, detect and assign weights for the training samples driven by the feedback of model performance and image quality, which ensures that our model gradually focuses on more challenging samples to achieve quality-adaptive deepfake detection. Extensive experiments show that compared with existing methods, ProFake improves deepfake detection accuracy by an average of over 10 % in real-world scenarios and by an average of over 30 % in heavily degraded scenarios, while maintaining comparable performance in detecting high-quality deepfakes.

The rise of deepfake technology poses significant threats to the authenticity of content on social media. This research introduces Sach-AI, a pioneering framework for detecting various deepfakes in video, audio, and image data. Leveraging the power of deep neural networks, Sach-AI utilizes Eulerian Video Magnification combined with the ResNext architecture for enhanced detection. For video deepfakes, Long Short-Term Memory (LSTM) networks are integrated to improve classification tasks. This combination allows Sach-AI to effectively address the evolving, multimodal nature of deepfakes. The framework has been rigorously evaluated using diverse datasets, such as Celeb-DF and FaceForensics , demonstrating its robustness and accuracy. Sach-AI achieved 97.76% accuracy in video deepfake detection, surpassing Intel’s FakeCatcher, 99.13% accuracy in audio deepfake detection, and 93.64% accuracy in image deepfake detection. These results underscore Sach-AI’s reliability in safeguarding digital media integrity against deceptive synthetic content in an era increasingly dominated by artificial technologies.

Plant fungal diseases are one of the most important causes of crop yield losses. Therefore, plant disease identification algorithms have been seen as a useful tool to detect them at early stages to mitigate their effects. Although deep-learning based algorithms can achieve high detection accuracies, they require large and manually annotated image datasets that is not always accessible, specially for rare and new diseases. This study focuses on the development of a plant disease detection algorithm and strategy requiring few plant images (Few-shot learning algorithm). We extend previous work by using a novel challenging dataset containing more than 100,000 images. This dataset includes images of leaves, panicles and stems of five different crops (barley, corn, rape seed, rice, and wheat) for a total of 17 different diseases, where each disease is shown at different disease stages. In this study, we propose a deep metric learning based method to extract latent space representations from plant diseases with just few images by means of a Siamese network and triplet loss function. This enhances previous methods that require a support dataset containing a high number of annotated images to perform metric learning and few-shot classification. The proposed method was compared over a traditional network that was trained with the cross-entropy loss function. Exhaustive experiments have been performed for validating and measuring the benefits of metric learning techniques over classical methods. Results show that the features extracted by the metric learning based approach present better discriminative and clustering properties. Davis-Bouldin index and Silhouette score values have shown that triplet loss network improves the clustering properties with respect to the categorical-cross entropy loss. Overall, triplet loss approach improves the DB index value by 22.7% and Silhouette score value by 166.7% compared to the categorical cross-entropy loss model. Moreover, the F-score parameter obtained from the Siamese network with the triplet loss performs better than classical approaches when there are few images for training, obtaining a 6% improvement in the F-score mean value. Siamese networks with triplet loss have improved the ability to learn different plant diseases using few images of each class. These networks based on metric learning techniques improve clustering and classification results over traditional categorical cross-entropy loss networks for plant disease identification.

The rapid advancement of digital content generation technologies has led to the proliferation of deepfake media, posing significant security and privacy risks on social networks. Conventional deepfake detection methods struggle with accuracy, computational efficiency, and adaptability to evolving fake image generation techniques. To address these challenges, we propose a novel deepfake detection framework that integrates an Adaptive deep convolutional generative adversarial network (Adaptive-DCGAN) with a lightweight convolutional neural network (Lite-CNN). The Adaptive-DCGAN generates high-quality synthetic images, enhancing the training dataset, while the Lite-CNN efficiently classifies real and fake images with minimal computational overhead. Experimental results demonstrate that the proposed model achieves 95% accuracy, significantly outperforming existing GAN-CNN-based detection systems, which only reach 53% accuracy. Additionally, the framework exhibits superior performance metrics, including 95% precision, recall, and F1-score, ensuring robust and reliable deepfake detection. The proposed system offers a scalable and efficient solution for securing digital media platforms against deepfake threats. Future work will focus on improving generalization across diverse datasets, incorporating advanced synthetic image generation techniques, and enabling real-time deepfake detection for large-scale social media applications.

A novel approach for accurate detection of the DDoS attacks in SDN-based SCADA systems based on deep recurrent neural networks

Abstract—Deepfake technology, driven by generative adversarial networks (GANs), poses significant challenges in digital security, misinformation, and privacy. Detecting deepfakes in images and videos requires advanced deep learning models. This study explores deepfake detection using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures like Vision Transformers (ViTs). We employ Meso4_DF deepfake detection pipeline that uses TensorFlow/Keras, PyTorch, OpenCV for processing, with Dlib, Scikit-Image, and NumPy for feature extraction, leveraging transfer learning for enhanced accuracy. Video analysis includes frame extraction and temporal feature learning using LSTMs and 3D CNNs. Experimental results demonstrate that deep learning-based methods achieve high accuracy in distinguishing real and manipulated media, offering a robust approach to deepfake detection. Keywords—Deepfake Detection,CNN,GAN,Meso4 model, Image Analysis,Video Analysis.

The rise of deep fake technology has ignited widespread societal apprehensions about potential security risks and the dissemination of false information. Despite extensive research into deepfake detection, effectively discerning low-quality deepfakes and simultaneously identifying variations in their quality remains a significant and formidable challenge. This investigation explores the dynamic field of deep fake detection, focusing specifically on video analysis targeting facial manipulations. The study introduces Celeb-DF, a substantial dataset comprising high- quality deep fake videos of celebrities, challenging prevailing detection methods. Additionally, a revolutionary Quality-Agnostic Deep Fake Detection (QAD) framework addresses the intricate task of simultaneously recognizing diverse qualities of deep fakes, surpassing established benchmarks across multiple datasets. The paper highlights ongoing efforts to enhance deep fake detection strategies, incorporating advanced models such as Stable Diffusion, and employing interpretability-through-prototypesn by merging fine-tuned Vision Transformers with Support Vector Machines. Keywords -- Deep fake technology, security risks, deep fake detection, Celeb-DF dataset, video analysis, Quality-Agnostic Deep Fake Detection (QAD), Stable Diffusion, Vision Transformers, Support Vector Machines.

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

DeepFake detection with multi-scale convolution and vision transformer

Learning a deep dual-level network for robust DeepFake detection

The rapid growth of deepfake technology, generating realistic manipulated media, poses a significant threat due to potential misuse. Therefore, effective detection methods are urgently needed to prevent malicious use, as current approaches often focus on single modalities or the simple fusion of audio–visual signals, limiting their accuracy. To solve this problem, we propose a deepfake detection scheme based on bimodal temporal feature prediction, which innovatively introduces the idea of temporal feature prediction into the audio–video bimodal deepfake detection task, aiming at fully exploiting the temporal laws of audio–visual modalities. First, pairs of adjacent audio–video sequence clips are used to construct input quadruples, and a dual-stream network is employed to extract temporal feature representations from video and audio, respectively. A video prediction module and an audio prediction module are designed to capture the temporal inconsistencies within each single modality by predicting future temporal features and comparing them with reference features. Then, a projection layer network is designed to align the audio–visual features, using contrastive loss functions to perform contrastive learning and maximize the differences between real and fake video modalities. Experiments on the FakeAVCeleb dataset demonstrate superior performance with an accuracy of 84.33% and an AUC of 89.91%, outperforming existing methods and confirming the effectiveness of our approach in deepfake detection.

The proliferation of the capability for producing and distributing deepfake videos threatens the integrity of systems of justice, democratic processes, and the general ability to critically assess evidence. This study sought to identify individual differences that predict one’s ability to detect these forgeries. It was hypothesized that measures of affect detection and political orientation would correlate with performance on a deepfake detection task. Within a sample (N = 173) of college undergraduates and participants from Amazon’s M-turk, affect detection ability was shown to correlate with deepfake detection ability, r(171) = .73, p < .001, and general orientation to the political left was shown to correlate with deepfake detection ability, r(171) = .42, p < .001. The results of this study serve to identify populations who are particularly susceptible to deception via deepfake video and to inform the development of interventions that may help defend against attempts to influence them.

In recent years, the abuse of a face swap technique called deepfake has raised enormous public concerns. So far, a large number of deepfake videos (known as deepfakes) have been crafted and uploaded to the internet, calling for effective countermeasures. One promising countermeasure against deepfakes is deepfake detection. Several deepfake datasets have been released to support the training and testing of deepfake detectors, such as DeepfakeDetection [1] and FaceForensics++ [23]. While this has greatly advanced deepfake detection, most of the real videos in these datasets are filmed with a few volunteer actors in limited scenes, and the fake videos are crafted by researchers using a few popular deepfake softwares. Detectors developed on these datasets may become less effective against real-world deepfakes on the internet. To better support detection against real-world deepfakes, in this paper, we introduce a new dataset WildDeepfake, which consists of 7,314 face sequences extracted from 707 deepfake videos collected completely from the internet. WildDeepfake is a small dataset that can be used, in addition to existing datasets, to develop and test the effectiveness of deepfake detectors against real-world deepfakes. We conduct a systematic evaluation of a set of baseline detection networks on both existing and our WildDeepfake datasets, and show that WildDeepfake is indeed a more challenging dataset, where the detection performance can decrease drastically. We also propose two (eg. 2D and 3D) Attention-based Deepfake Detection Networks (ADDNets) to leverage the attention masks on real/fake faces for improved detection. We empirically verify the effectiveness of ADDNets on both existing datasets and WildDeepfake. The dataset is available at: https://github.com/deepfakeinthewild/deepfake-in-the-wild.