Top Research Papers on Deepfake Detection

DeepMark is proposed, a scalable and robust framework for detecting DeepFakes that imprints essential visual features of a video into DeepMark Meta (DMM) and uses it to detect DeepFake manipulations by comparing the extracted visual features with the ground truth in DMM.

This paper aims to investigate deepfake challenges, and to detect deepfake videos by using eye blinking, using convolutional neural networks to classify the eye states and long short term memory for sequence learning and the eye aspect ratio was used to calculate the height and width of open and closed eyes and to detects the blinking intervals.

A detection model using convolution neural network (CNN) for face detection and Recurrent neural network (RNN) for video classification is developed for detecting fake videos.

This study proposes a pluggable and efficient active model watermarking framework for Deepfake detection that leverages the universal convolutional structure in generative model decoders and introduces convolutional kernel normalization to seamlessly integrate watermark parameters with those of the generative model.

This paper proposes a binary classifier based on a 2-phase learning architecture for detecting DeepFake images and demonstrates 91% validation accuracy on a large, diverse dataset of sophisticated GAN-generated DeepFake images.

This study conducts a comparative analysis of three varied convolutional neural networks (CNNs) for deepfake image detection by comparing three major CNN architectures and highlights the strengths and weaknesses of each CNN architecture.

The goal of the research is to create a deep learning-based method for identifying deepfake videos that can differentiate between real and fake information by using deep learning techniques and a variety of datasets for training, which helps combat the proliferation of false visual media.

This research introduces a Multimodal Deepfake Detection system capable of identifying manipulated content by combining visual and auditory cues, which employs convolutional neural networks to analyse video frames and process audio spectrograms, providing a comprehensive approach to detecting deepfake content.

This paper proposes a holistic strategy employing Facenet_pytorch, MTCNN, and InceptionResnetV1 for robust deepfake detection, achieving significant strides in differentiating manipulated content from authentic media, contributing to the ethical deployment of deepfake detection technologies.

This is the first time to employ automated machine learning to adaptively search a neural architecture for deepfake detection, and based on the explored search space, this method achieves competitive prediction accuracy compared to previous methods.

This research proposes a robust deep learning-based technique for detecting deep fakes in videos, designed to automatically identify several forms of deep forgery, including replacement and re-enactment techniques.

This article explores the creation and detection of deepfakes and provides an in-depth view as to how these architectures work and the current trends and advancements in this domain.

A study of the perceptions, current procedures, and expectations of journalists regarding a system for detection of deepfake videos, which combines technical knowledge of media forensics and the study to design a system usable by, and useful for, journalists.

This paper proposes the first parameter-efficient tuning approach for deepfake detection, namely DeepFake-Adapter, to effectively and efficiently adapt the generalizable high-level semantics from large pre-trained ViTs to aid deepfake detection.

In a fake video, the face, expression or speech is replaced with an image of another's face, with a distinct speech or emotion with the help of the technology of deep learning.

Artificial intelligence has gone crazy in such a way that any ordinary man with technical knowledge could, in a sense, be poised to create an artificial deepfake simply by observing facial movements. The more the videos appear, the more realistic they become with these technologies. This presently presents a significant challenge with the rise of fake news, which, although seemingly bad for social networks, is currently one big technological threat in the spread of deepfakes online. Deepfakes pose allsided danger such as manipulation, misinformation, humiliation, and defamation. In this resear...

Analytic thinking and political interest were positively associated with identifying deepfakes and negatively associated with the perceived accuracy of a fake news piece about a leaked video (whether or not the deepfake video itself was presented).

EfficientNet-B5 is used to pluck out the spatial options of those faces they are fed as a batch of input series into a two-way long- and short-term memory (BiLSTM) to extract temporal characteristics.

A novel DEEPFAKE C-L-I (Classification-Localization-Inference) in which the idea of accelerating Quantized Deepfake Detection Models using FPGAs due to their ability of maximum parallelism and energy efficiency compared to generalized GPUs is explored.

This work develops an open-source online platform, known as DeepFake-o-meter, that integrates state-of-the-art DeepFake detection methods and provide a convenient interface for the users.

The current development status of deepfake detection technology is introduced, its principles and methods are presented in detail, and researchers look ahead to its future development directions.

It is shown that deepfake detectors proven to generalize well on multiple research datasets still struggle in real-world scenarios with well-crafted fakes, and it is suggested that their training datasets should be complemented by high-quality fakes since training on mere research data is insufficient.

This work introduces an open-source and user-friendly online platform, DeepFake-O-Meter v2.0, that integrates state-of-the-art methods for detecting DeepFake images, videos, and audio and serves as an evaluation and benchmanrking platform for researchers in digital media forensics to compare the performance of multiple algorithms on the same input.

A large-scale empirical study of facial deepfake/detection models and a set of key findings are drawn that the detection methods have poor generalization on samples generated by different deepfake methods, and there is no significant correlation between anti-detection ability and visual quality of deepfake samples.

This paper proposes an approach for detecting deepfake videos using Resnext CNN and LSTM, which can help in preventing the spread of misinformation and safeguarding the authors' society.

No single method can reliably detect all Deepfakes and, therefore, combining multiple methods is often necessary to achieve high detection rates, and it is suggested that more extensive and diverse datasets are needed to improve the accuracy of detection algorithms.

This study proposes a novel deepfake detection system that integrates a hybrid Recurrent Neural Network, Convolutional Neural Network, and Long Short-Term Memory architecture, employing a multi-faceted approach to training by utilizing several diverse datasets encompassing real and synthetic videos.

A wild testbed is built by gathering texts from various human writings and deepfake texts generated by different LLMs to showcase the challenges of deep-fake text detection in a wild testbed and show empirical results on automatic detection meth-ods.

This paper uses large margin cosine loss function (LMCL) and online frequency masking augmentation to force the neural network to learn more robust feature embeddings and achieves the lowest equal error rate (EER) among all single-system submissions during the ASVspoof 2019 challenge.

This work presents an alternative approach to DeepFake detection that focuses on whether the identity in the suspect image/video is true, and presents a simple identity-based detection algorithm called the OuterFace, which achieves superior detection accuracy and generalizes well to different DeepFake methods, and is robust with respect to video degradation techniques.

Various techniques that are used by several researchers to detect Deepfake videos, based on Machine Learning, Support Vector Machine and Deep Learning based techniques such as Convolution Neural Network with or without LSTM are analyzed.

Evaluated methods of deepfake detection are evaluated and how they can be combined or modified to get more accurate results are discussed.

The development of DeepFakes generation and detection technology is reviewed, and the existing approaches and datasets are systematically summarized and scientifically classified in order to provide a reference for further research in the field ofDeepFakes detection.

The proposed approach results in accuracy scores comparable to and surpassing several SOTA(State-of-the-art) approaches on three benchmark datasets, while consuming considerably lesser computational overhead, and containing over 100x lesser trainable parameters which was achieved using the extraction and manipulation of geometrical features.

This work has proposed a novel technique for recognizing FaceSwap-based deepfakes using landmarks computed from the input videos by employing Dlib-library and demonstrates that SVM works well than ANN in classifying the manipulated samples due to its power to deal with over-fitted training data.

A solution based on a GAN discriminator to detect DeepFake videos, which can be used to manipulate public opinion during elections, commit fraud, and discredit or blackmail people is explored.

This study introduces a deepfake detection technique that enhances interpretability using the network dissection algorithm, providing a better understanding of deep neural networks’ hierarchical structures and decision processes.

It has been revealed that current detection methods are still insufficient to be applied in real scenes, and further research should pay more attention to the generalization and robustness.

The survey shows that future research should address the lack of large scale datasets in the wild, poor generalization of existing detection methods to unknown fake attacks, as well as interpretability of detection results.

The Face Deepfake Detection and Reconstruction Challenge was described and two different tasks were proposed to the participants: creating a Deepfake detector capable of working in an “in the wild” scenario; and creating a method capable of reconstructing original images from Deepfakes.

This work performs the first large scale test of PRNU-based deepfake detection methods on a variety of standard datasets and shows the impact of often neglected parameters of the face extraction stage on detection accuracy.

A key component of FaceGuard is a new deep-learning-based watermarking method, which is robust to normal image post-processing such as JPEG compression, Gaussian blurring, cropping, and resizing, but fragile to deepfake manipulation.

The 1M-Deepfakes Detection Challenge is designed to engage the research community in developing advanced methods for detecting and localizing deepfake manipulations within the large-scale high-realistic audio-visual dataset.

A simple and efficient pipeline for detecting fake environmental sounds based on the CLAP audio embedding is proposed and it is shown that using an audio embedding trained specifically on environmental audio is beneficial over a standard VGGish one as it provides a 10% increase in detection performance.

This talk will describe the most reliable deep learning-based approaches for detecting deepfakes, with a focus on those that enable domain generalization, with reference to realistic scenarios, such as the dissemination of manipulated images and videos on social networks.

The Two-Stream models, which concurrently process spatial and temporal features, demonstrate superior performance in identifying deepfake videos, thereby underscoring the significance of temporal dynamics in deepfake detection technology.

This talk will describe the most reliable deep learning-based approaches for detecting deepfakes, with a focus on those that enable domain generalization, with reference to realistic scenarios, such as the dissemination of manipulated images and videos on social networks.

It is argued that the future deepfake detection algorithm will be more accurate, which can further maintain the authenticity of network information and social stability.

This survey systematically analyzes more than 200 papers published up to March 2024 and identifies the current state-of-the-art speech Deepfake detection systems, offering clear guidance for researchers aiming to enhance speech Deepfake detection systems.

A simple deep learning model in combination with word embeddings is employed for the classification of tweets as human-generated or bot-generated using a publicly available Tweepfake dataset displaying the effectiveness and highlighting its advantages in accurately addressing the task at hand.