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

Ismail, Aya Abdelsalam; Elpeltagy, Marwa S.; Zaki, Mervat S.; ElDahshan, Kamal A.

doi:10.3390/s21165413

Cited by 59 publications

(28 citation statements)

References 45 publications

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“…Apart from the research works discussed in Table 5, several researchers have made use of the publicly available benchmark datasets for deepfakes. The authors of [1,10,71,[76][77][78]98] used benchmark datasets shown in Table 5. The benchmark dataset VidTIMIT [115] consists of 35 persons speaking brief words on video and accompanying audio recordings.…”

Section: Discussionmentioning

confidence: 99%

“…A YOLO-CNN-XGBoost model is presented in [10] for deepfake detection. It incorporates a CNN, extreme gradient boosting (XGB), and the face detector you only look once (YOLO).…”

Section: Deepfake Video Detection Using Image Processing Techniquesmentioning

confidence: 99%

“…Faces are essential in human interactions as well as biometric-based human authentication and identity services. As a result, convincing changes in faces have the potential to undermine security applications and digital communications [10]. Deepfake technology is used to make several kinds of videos such as funny or pornographic videos of a person involving the voice and video of a person without any authorized use [11,12].…”

Section: Introductionmentioning

confidence: 99%

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A Review of Image Processing Techniques for Deepfakes

Shahzad

Rustam

Flores

et al. 2022

Sensors

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Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets.

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Section: Discussionmentioning

confidence: 99%

“…A YOLO-CNN-XGBoost model is presented in [10] for deepfake detection. It incorporates a CNN, extreme gradient boosting (XGB), and the face detector you only look once (YOLO).…”

Section: Deepfake Video Detection Using Image Processing Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review of Image Processing Techniques for Deepfakes

Shahzad

Rustam

Flores

et al. 2022

Sensors

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“…Deepfake Detection Methods: Deepfake detection methods are still a trending topic in the research area. Several stateof-the-art deepfake detection methods that have been proposed mostly rely on flaws in deepfake generation pipelines, i.e., visual artifacts or discrepancies such as different skin tones and jerky movements [8]. Subtle transformations in generating deepfakes are becoming more elusive to detect.…”

Section: Related Workmentioning

confidence: 99%

Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors

Lim¹,

Kuan²,

Pu³

et al. 2022

Preprint

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Deepfakes utilise Artificial Intelligence (AI) techniques to create synthetic media where the likeness of one person is replaced with another. There are growing concerns that deepfakes can be maliciously used to create misleading and harmful digital contents. As deepfakes become more common, there is a dire need for deepfake detection technology to help spot deepfake media. Present deepfake detection models are able to achieve outstanding accuracy (>90%). However, most of them are limited to within-dataset scenario, where the same dataset is used for training and testing. Most models do not generalise well enough in cross-dataset scenario, where models are tested on unseen datasets from another source. Furthermore, stateof-the-art deepfake detection models rely on neural networkbased classification models that are known to be vulnerable to adversarial attacks. Motivated by the need for a robust deepfake detection model, this study adapts metamorphic testing (MT) principles to help identify potential factors that could influence the robustness of the examined model, while overcoming the test oracle problem in this domain. Metamorphic testing is specifically chosen as the testing technique as it fits our demand to address learning-based system testing with probabilistic outcomes from largely black-box components, based on potentially large input domains. We performed our evaluations on MesoInception-4 and TwoStreamNet models, which are the state-of-the-art deepfake detection models. This study identified makeup application as an adversarial attack that could fool deepfake detectors. Our experimental results demonstrate that both the MesoInception-4 and TwoStreamNet models degrade in their performance by up to 30% when the input data is perturbed with makeup.

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“…Zanthoxylum fruit target detection is similar to the majority of target detection programs in many aspects, such as UAVS automatic navigation, fire detection and face recognition. Therefore, traditional detection models, such as R-CNN [18][19][20], Faster R-CNN [21], YOLO [22][23][24][25] and SSD [26], have been applied to the detection of Zanthoxylum. Among these models, R-CNN, SSP-NET and Faster R-CNN have two detection stages, with high accuracy but much slower computing speed than YOLO and SSD models with primary structures.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture

Huang

et al. 2022

Sensors

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The target recognition algorithm is one of the core technologies of Zanthoxylum pepper-picking robots. However, most existing detection algorithms cannot effectively detect Zanthoxylum fruit covered by branches, leaves and other fruits in natural scenes. To improve the work efficiency and adaptability of the Zanthoxylum-picking robot in natural environments, and to recognize and detect fruits in complex environments under different lighting conditions, this paper presents a Zanthoxylum-picking-robot target detection method based on improved YOLOv5s. Firstly, an improved CBF module based on the CBH module in the backbone is raised to improve the detection accuracy. Secondly, the Specter module based on CBF is presented to replace the bottleneck CSP module, which improves the speed of detection with a lightweight structure. Finally, the Zanthoxylum fruit algorithm is checked by the improved YOLOv5 framework, and the differences in detection between YOLOv3, YOLOv4 and YOLOv5 are analyzed and evaluated. Through these improvements, the recall rate, recognition accuracy and mAP of the YOLOv5s are 4.19%, 28.7% and 14.8% higher than those of the original YOLOv5s, YOLOv3 and YOLOv4 models, respectively. Furthermore, the model is transferred to the computing platform of the robot with the cutting-edge NVIDIA Jetson TX2 device. Several experiments are implemented on the TX2, yielding an average time of inference of 0.072, with an average GPU load in 30 s of 20.11%. This method can provide technical support for pepper-picking robots to detect multiple pepper fruits in real time.

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

Cited by 59 publications

References 45 publications

A Review of Image Processing Techniques for Deepfakes

A Review of Image Processing Techniques for Deepfakes

Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors

A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture

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