A Multilevel Single Stage Network for Face Detection

Hui, Kanghua; Wang, Jin; Huang, He; Ip, W. H.

doi:10.1155/2021/5582132

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…This paper introduces a new efficient architecture, YOLO-InceptionResNetV2-XGBoost (YIX), which discovers the visual discrepancies and artifacts within video frames and then judges whether a given video is real or a deepfake. The combination of these three methods is justified as follows: The YOLO detector proves its efficiency in object detection and face recognition systems over the state-of-the-art detectors [ 10 , 11 ] since it has a good trade-off between performance and speed [ 12 , 13 ]. Additionally, it is characterized by its ability to produce fewer false positives in the background [ 14 ], thus improving the detection method performance.…”

Section: Introductionmentioning

confidence: 99%

A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost

Ismail

Elpeltagy

Zaki

et al. 2021

Sensors

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Currently, face-swapping deepfake techniques are widely spread, generating a significant number of highly realistic fake videos that threaten the privacy of people and countries. Due to their devastating impacts on the world, distinguishing between real and deepfake videos has become a fundamental issue. This paper presents a new deepfake detection method: you only look once–convolutional neural network–extreme gradient boosting (YOLO-CNN-XGBoost). The YOLO face detector is employed to extract the face area from video frames, while the InceptionResNetV2 CNN is utilized to extract features from these faces. These features are fed into the XGBoost that works as a recognizer on the top level of the CNN network. The proposed method achieves 90.62% of an area under the receiver operating characteristic curve (AUC), 90.73% accuracy, 93.53% specificity, 85.39% sensitivity, 85.39% recall, 87.36% precision, and 86.36% F1-measure on the CelebDF-FaceForencics++ (c23) merged dataset. The experimental study confirms the superiority of the presented method as compared to the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost

Ismail

Elpeltagy

Zaki

et al. 2021

Sensors

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“…In recent years, the extraction and analysis of video information has become an important research content in video processing, which is of great significance in video semantic extraction, video query, and other aspects. Character detection and background detection, which are similar to scene information detection, have been deeply studied and widely applied [1][2][3][4][5][6][7][8][9]. However, there are not many indepth researches on video situational information.…”

Section: Introductionmentioning

confidence: 99%

Video Scene Information Detection Based on Entity Recognition

Qian

Dai

et al. 2021

Wireless Communications and Mobile Computing

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Video situational information detection is widely used in the fields of video query, character anomaly detection, surveillance analysis, and so on. However, most of the existing researches pay much attention to the subject or video backgrounds, but little attention to the recognition of situational information. What is more, because there is no strong relation between the pixel information and the scene information of video data, it is difficult for computers to obtain corresponding high-level scene information through the low-level pixel information of video data. Video scene information detection is mainly to detect and analyze the multiple features in the video and mark the scenes in the video. It is aimed at automatically extracting video scene information from all kinds of original video data and realizing the recognition of scene information through “comprehensive consideration of pixel information and spatiotemporal continuity.” In order to solve the problem of transforming pixel information into scene information, this paper proposes a video scene information detection method based on entity recognition. This model integrates the spatiotemporal relationship between the video subject and object on the basis of entity recognition, so as to realize the recognition of scene information by establishing mapping relation. The effectiveness and accuracy of the model are verified by simulation experiments with the TV series as experimental data. The accuracy of this model in the simulation experiment can reach more than 85%.

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A novel approach for face recognition using biogeography based optimization with extinction and evolution

Goel

2022

Multimed Tools Appl

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A Multilevel Single Stage Network for Face Detection

Cited by 3 publications

References 33 publications

A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost

A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost

Video Scene Information Detection Based on Entity Recognition

A novel approach for face recognition using biogeography based optimization with extinction and evolution

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