Audio forgery is any act of tampering, illegal copy and fake quality in the audio in a criminal way. In the last decade, there has been increasing attention to the audio forgery detection due to a significant increase in the number of forge in different type of audio. There are a number of methods for forgery detection, which electric network frequency (ENF) is one of the powerful methods in this area for forgery detection in terms of accuracy. In spite of suitable accuracy of ENF in a majority of plug-in powered devices, the weak accuracy of ENF in audio forgery detection for battery-powered devices, especially in laptop and mobile phone, can be consider as one of the main obstacles of the ENF. To solve the ENF problem in terms of accuracy in battery-powered devices, a combination method of ENF and phase feature is proposed. From experiment conducted, ENF alone give 50% and 60% accuracy for forgery detection in mobile phone and laptop respectively, while the proposed method shows 88% and 92% accuracy respectively, for forgery detection in battery-powered devices. The results lead to higher accuracy for forgery detection with the combination of ENF and phase feature.
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http://dx.doi.org/10.1016/j.forsciint.2016.07.001 | DOI Listing |
Sci Data
December 2024
Computer Vision Centre, Bellaterra, 08193, Spain.
This paper presents a new synthetic dataset of ID and travel documents, called SIDTD. The SIDTD dataset is created to help training and evaluating forged ID documents detection systems. Such a dataset has become a necessity as ID documents contain personal information and a public dataset of real documents can not be released.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Computer Engineering, Jinling Institute of Technology, Nanjing, Jiangsu, China.
With the advancement of deep forgery techniques, particularly propelled by generative adversarial networks (GANs), identifying deepfake faces has become increasingly challenging. Although existing forgery detection methods can identify tampering details within manipulated images, their effectiveness significantly diminishes in complex scenes, especially in low-quality images subjected to compression. To address this issue, we proposed a novel deep face forgery video detection model named Two-Stream Feature Domain Fusion Network (TSFF-Net).
View Article and Find Full Text PDFHeliyon
February 2024
Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India.
Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to detect deepfake has become a pressing need.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China.
With the advancement of deepfake forgery technology, highly realistic fake faces have posed serious security risks to sensor-based facial recognition systems. Recent deepfake detection models mainly use binary classification models based on deep learning. Despite achieving high detection accuracy on intra-datasets, these models lack generalization ability when applied to cross-datasets.
View Article and Find Full Text PDFNeural Netw
February 2025
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710048, China. Electronic address:
The continuous advancement of face forgery techniques has caused a series of trust crises, posing a significant menace to information security and personal privacy. In response, deep learning is being employed to develop effective detection methods to identify deepfake images and videos. Currently, most detection methods generally achieve satisfactory performance in intra-domain detection.
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