Wavelet-Based Texture Mining and Enhancement for Face Forgery Detection

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xin Li, Hui Zhao, Bingxin Xu, Hongzhe Liu
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引用次数: 0

Abstract

Due to the abuse of deep forgery technology, the research on forgery detection methods has become increasingly urgent. The corresponding relationship between the frequency spectrum information and the spatial clues, which is often neglected by current methods, could be conducive to a more accurate and generalized forgery detection. Motivated by this inspiration, we propose a wavelet-based texture mining and enhancement framework for face forgery detection. First, we introduce a frequency-guided texture enhancement (FGTE) module that mining the high-frequency information to improve the network’s extraction of effective texture features. Next, we propose a global–local feature refinement (GLFR) module to enhance the model’s leverage of both global semantic features and local texture features. Moreover, the interactive fusion module (IFM) is designed to fully incorporate the enhanced texture clues with spatial features. The proposed method has been extensively evaluated on five public datasets, such as FaceForensics++ (FF++), deepfake (DF) detection (DFD) challenge (DFDC), Celeb-DFv2, DFDC preview (DFDC-P), and DFD, for face forgery detection, yielding promising performance within and cross dataset experiments.

Abstract Image

基于小波的纹理挖掘与增强人脸伪造检测
由于深度伪造技术的滥用,伪造检测方法的研究日益迫切。频谱信息与空间线索之间的对应关系是当前方法经常忽略的,有助于更准确、更广义的伪造检测。受此启发,我们提出了一种基于小波的纹理挖掘和增强框架,用于人脸伪造检测。首先,引入频率引导纹理增强(FGTE)模块,挖掘高频信息,提高网络对有效纹理特征的提取;接下来,我们提出了一个全局-局部特征细化(GLFR)模块,以增强模型对全局语义特征和局部纹理特征的利用。设计了交互式融合模块(IFM),将增强的纹理线索与空间特征充分融合。所提出的方法已经在5个公共数据集上进行了广泛的评估,如facefrensics ++ (FF++)、deepfake (DF) detection (DFD) challenge (DFDC)、Celeb-DFv2、DFDC preview (DFDC- p)和DFD,用于人脸伪造检测,在数据集内部和跨数据集实验中产生了令人满意的性能。
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来源期刊
IET Biometrics
IET Biometrics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
0.00%
发文量
46
审稿时长
33 weeks
期刊介绍: The field of biometric recognition - automated recognition of individuals based on their behavioural and biological characteristics - has now reached a level of maturity where viable practical applications are both possible and increasingly available. The biometrics field is characterised especially by its interdisciplinarity since, while focused primarily around a strong technological base, effective system design and implementation often requires a broad range of skills encompassing, for example, human factors, data security and database technologies, psychological and physiological awareness, and so on. Also, the technology focus itself embraces diversity, since the engineering of effective biometric systems requires integration of image analysis, pattern recognition, sensor technology, database engineering, security design and many other strands of understanding. The scope of the journal is intentionally relatively wide. While focusing on core technological issues, it is recognised that these may be inherently diverse and in many cases may cross traditional disciplinary boundaries. The scope of the journal will therefore include any topics where it can be shown that a paper can increase our understanding of biometric systems, signal future developments and applications for biometrics, or promote greater practical uptake for relevant technologies: Development and enhancement of individual biometric modalities including the established and traditional modalities (e.g. face, fingerprint, iris, signature and handwriting recognition) and also newer or emerging modalities (gait, ear-shape, neurological patterns, etc.) Multibiometrics, theoretical and practical issues, implementation of practical systems, multiclassifier and multimodal approaches Soft biometrics and information fusion for identification, verification and trait prediction Human factors and the human-computer interface issues for biometric systems, exception handling strategies Template construction and template management, ageing factors and their impact on biometric systems Usability and user-oriented design, psychological and physiological principles and system integration Sensors and sensor technologies for biometric processing Database technologies to support biometric systems Implementation of biometric systems, security engineering implications, smartcard and associated technologies in implementation, implementation platforms, system design and performance evaluation Trust and privacy issues, security of biometric systems and supporting technological solutions, biometric template protection Biometric cryptosystems, security and biometrics-linked encryption Links with forensic processing and cross-disciplinary commonalities Core underpinning technologies (e.g. image analysis, pattern recognition, computer vision, signal processing, etc.), where the specific relevance to biometric processing can be demonstrated Applications and application-led considerations Position papers on technology or on the industrial context of biometric system development Adoption and promotion of standards in biometrics, improving technology acceptance, deployment and interoperability, avoiding cross-cultural and cross-sector restrictions Relevant ethical and social issues
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