FAMM: Facial Muscle Motions for Detecting Compressed Deepfake Videos Over Social Networks

Liao, Xin; Wang, Yumei; Wang, Tianyi; Hu, Jing; Wu, Xiaoshuai

doi:10.1109/tcsvt.2023.3278310

Cited by 23 publications

(2 citation statements)

References 37 publications

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“…The popularity of multimedia content on the Web has gradually raised a series of security issues. For example, the malicious modification of content, forgery 24 and so on. This has given rise to a series of related security technologies 25 .…”

Section: Related Workmentioning

confidence: 99%

A generalized detection framework for covert timing channels based on perceptual hashing

Zhuang,

Chen,

Tian

2024

Trans Emerging Tel Tech

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Network covert channels use network resources to transmit data covertly, and their existence will seriously threaten network security. Therefore, an effective method is needed to prevent and detect them. Current network covert timing channel detection methods often incorporate machine learning methods in order to achieve generalized detection, but they consume a large amount of computational resources. In this paper, we propose a generalized detection framework for covert channels based on perceptual hashing without relying on machine learning methods. And we propose a one‐dimensional data feature descriptor for feature extraction of perceptual hash for the data characteristics of covert timing channels. We first generate the hash sequence of the corresponding channel to get the average hash, which is used for comparison in the test phase. The experimental results show that the feature descriptor can capture the feature differences of one‐dimensional data well. When compared to machine learning methods, this perceptual hashing algorithms enable faster traffic detection. Meanwhile, our method is able to detect the effectiveness with the smallest coverage window compared with the latest solutions. Moreover, it exhibits robustness in jitter network environment.

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Section: Related Workmentioning

confidence: 99%

A generalized detection framework for covert timing channels based on perceptual hashing

Zhuang,

Chen,

Tian

2024

Trans Emerging Tel Tech

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“…Among deep-learning-based methods [6,7,22,[47][48][49][50][51], TCDCN [47] represents an early 2D facial landmark detection model using deep learning; it demonstrates superior performance compared with previous nonparametric methods, even with a simplistic CNN layer structure. In contrast, FAN [48] adopts the stacked hourglass network structure to enable 2D and 3D facial landmark detection.…”

Section: Facial Landmark Detectionmentioning

confidence: 99%

Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation

Lee,

Jang,

Bae

et al. 2024

Sensors

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Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications.

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A review on deepfake generation and detection: bibliometric analysis

Kaushal,

Kumar,

Kumar

2024

Multimed Tools Appl

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FAMM: Facial Muscle Motions for Detecting Compressed Deepfake Videos Over Social Networks

Cited by 23 publications

References 37 publications

A generalized detection framework for covert timing channels based on perceptual hashing

A generalized detection framework for covert timing channels based on perceptual hashing

Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation

A review on deepfake generation and detection: bibliometric analysis

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