This research systematically analyzes the generative mechanisms and detection techniques ofDeepfake, evaluating the effectiveness and limitations of mainstream approaches such as frequency domain analysis and physiological signal recognition and proposes forward-looking solutions to address future challenges.
This paper systematically reviews the types of audio forgery, detection principles, authoritative datasets and evaluation indicators, compares and analyzes traditional detection methods with deep learning detection techniques, and points out the core challenges in generalization, robustness, etc. of current methods.
Ming-Lei Zhu· Mathematical Modeling and Al...· 0 citations
Deepfake technology has advanced swiftly, enabling the rapid production of hyper-realistic synthetic media that pose considerable threats to digital security, privacy, military operations, and information integrity. This paper extensively examines visual intelligence and computer vision methodologies for deepfake detec...
Alexandros Gazis, Stylianos Pappas, T. Vavouras et al.· ICCK Transactions on Sensing...· 0 citations
Fighting the risks associated with deepfakes requires comprehensive legal reforms, international cooperation, robust AI governance frameworks, technological detection mechanisms, digital literacy initiatives, and balanced regulation that simultaneously protects innovation, fundamental rights, and democracy.
R. Author· Global Journal of Computing...· 0 citations
This paper presents a comprehensive survey of the deepfake ecosystem, offering a structured taxonomy of both generation paradigms and counter-forensic detection mechanisms, and critically examine state-of-the-art synthesis methodologies.
Shobhit Tyagi, Naveen Chauhan, Akshit Raj Patel et al.· Multimedia tools and applica...· 0 citations
The rapid advancement of deep learning-based generative models has enabled the creation of highly realistic synthetic media, commonly referred to as deepfakes. While these technologies have legitimate applications, their misuse poses serious threats to digital trust, identity protection, and information authenticity. E...
Al Mulla, R. Arora, Arya Chavan et al.· International Workshop on Ar...· 0 citations
The rapid advancements in generative adversarial networks (GANs) have led to the production of highly realistic synthetic images, posing severe threats to the credibility and authenticity of digital media across social platforms, news outlets, and official documents. Passive detection methods tackle this problem by ide...
Lin-Jie Lu· ITM Web of Conferences· 0 citations
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