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The pervasive prevalence of DeepFakes poses a profound threat to individual privacy and the stability of society. Believing the synthetic videos of a celebrity and trumping up impersonated forgery videos as authentic are just a few consequences generated ...

DeepFake detection is pivotal in personal privacy and public safety. With the iterative advancement of DeepFake techniques, high-quality forged videos and images are becoming increasingly deceptive. Prior research has seen numerous attempts by ...

With the rapid and continuous development of AIGC, It is becoming increasingly difficult to distinguish between real and forged facial images, which calls for efficient forgery detection systems. Although many detection methods have noticed the ...

• To address the problems of low-quality datasets and poor detection performance across datasets, this study proposes MSIDSnet.
• The multi-scale fusion (MSF) module designed in this study can effectively obtain forged facial features, and ...

DeepFake face forgery has a serious negative impact on both society and individuals. Therefore, research on DeepFake detection technologies is necessary. At present, DeepFake detection technology based on deep learning has achieved acceptable ...

Deepfake detection has recently become an urgent issue since the deepfake technology has raised security concerns in society. However, current deepfake detection methods exist susceptibility when encountering unseen data, limiting their ...

The DeepFake, which generates synthetic content, has sparked a revolution in the fight against deception and forgery. However, most existing DeepFake detection methods mainly focus on improving detection performance with high-quality data while ...

• We explore the difference between uncompressed and high- compressed deepfake data.
• A High-Frequency Enhancement network is proposed for deepfake detection.
• A two-stage cross-fusion strategy is designed for information ...

The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques. While many recent works have taken essential steps on GAN-generated faces, more threatening attacks ...

The harmful utilization of DeepFake technology poses a significant threat to public welfare, precipitating a crisis in public opinion. Existing detection methodologies, predominantly relying on convolutional neural networks and deep learning ...

• We build an artifact and texture inconsistency detector for deepfake detection.
• Texture coherence is analyzed via a novel mask extraction method and an autoencoder.
• The use of an ensemble training strategy improved the ability to ...

Explainable Artificial Intelligence (XAI) attempts to help humans understand machine learning decisions better and has been identified as a critical component toward increasing the trustworthiness of complex black-box systems, such as deep neural ...

Nowadays, numerous synthesized images and videos generated by facial manipulated techniques have become an emerging problem, which promotes facial manipulation detection to be a significant topic. Much concern about the use of synthesized facial ...

All the existing models for DeepFake detection focus on plaintext faces. However, outsourced computing is usually considered in practical applications for DeepFake detection and the input data may contain private and sensitive information. Thus, ...

This issue of DeepFake technique that may cause great threat to privacy, democracy, and national security has attracted the attention of deep learning researchers. DeepFake detection, therefore, has been a very hot issue in deep learning ...

• Propose a manipulated face detection model which predicts fake face in pix-lever.
• Facial geometry feature maps are used in predicting fake face.
• Propose a CNN-LSTM network to discriminate manipulated face images.

To reduce the harm of forged information, more and more detection methods use frequency domain information. They mostly take spectra as clues to identify fake content. However, the current work tends to use only one of the magnitude and phase ...

With the development of convolutional neural networks (CNN) and generative adversarial networks (GAN) in recent years, classifying fake videos produced through DeepFake has become a very difficult task. Most previous studies on DeepFake Detection were ...

Nowadays, synthesizing realistic fake face images and videos becomes easy benefiting from the advance in generation technology. With the popularity of face forgery, abuse of the technology occurs from time to time, which promotes the research on face ...

Face forgery detection has become a research hotspot due to security concerns about spreading ultrarealisitc fake faces over social platforms. However, most existing deep learning-based approaches fail to generalize in cross-dataset scenarios ...

Since photorealistic faces can be readily generated by facial manipulation technologies nowadays, potential malicious abuse of these technologies has drawn great concerns. Numerous deepfake detection methods are thus proposed. However, existing ...

• We alleviate the adverse impact of an imbalanced dataset by introducing the AUC loss to directly maximize the AUC score and minimize the adverse impact of an imbalanced dataset.
• We combine the features of both frame-level and video-...

Face manipulation techniques, especially DeepFake techniques, are causing severe social concerns and security problems. When faced with skewed data distributions such as those found in the real world, existing DeepFake detection methods exhibit ...

With the development of deep learning, AI-synthesized techniques, such as DeepFake, are widely spread on the Internet. Although many state-of-the-art detection methods have been able to obtain a good detection performance, most neural ...

The spread of face forgery videos is a serious threat to information credibility, calling for effective detection algorithms to identify them. Most existing methods have assumed a shared or centralized training set. However, in practice, data may be ...