About -A Secure Deepfake Mitigation Framework: Architecture, Issues, Challenges, and Societal Impact, Peer-Reviewed Academic Research Article,Cyber Security and Applications, Vol. 2 (2024), Article 100040

A Secure Deepfake Mitigation Framework: Architecture, Issues, Challenges, and Societal Impact examines the growing cybersecurity and societal risks created by AI-generated synthetic media and proposes a cryptographically secured framework for authenticating digital content and its source.

The authors describe deepfakes as artificially generated or altered video, audio, and imagery capable of impersonating real people, spreading false information, facilitating identity theft and financial fraud, damaging reputation, and eroding public confidence in media and other sources of information. The article emphasizes that deepfake creation and detection are developing in parallel, producing an ongoing technical contest in which no single detection method is sufficient.

The paper surveys several complementary detection approaches, including analysis of visual and audio inconsistencies, verification against trusted sources, forensic examination of digital content, machine-learning-based detection, watermarking and blockchain-related provenance techniques, and human expert review. It also discusses the principal AI architectures involved in deepfake generation and detection, including generative adversarial networks, recurrent neural networks, convolutional neural networks, transformers, and autoencoders.

Its principal technical contribution is a proposed secure deepfake mitigation framework combining source verification, content authentication, digital signatures, encryption, and Elliptic Curve Cryptography. The proposed architecture contemplates registration of content providers and requesters, mutual authentication and secure session establishment, protected content delivery, and cryptographic verification of both the digital content and its source.

The authors analyze the framework using the Dolev-Yao and CK adversary models and perform formal protocol verification using the Scyther security-analysis tool. The reported Scyther analysis found the tested secrecy, synchronization, agreement, and related security claims satisfied within the modeled bounds.The paper also includes a practical implementation using Python and Google Colab across three simulated deployment scenarios. The authors measure message volume, exchanged-data size, and computation time as the number of participating content providers and requesters increases.

The article Secure Deepfake Mitigation Framework: Architecture, Issues, Challenges, and Societal Impact is particularly relevant to questions of identity authentication, digital-evidence provenance, synthetic-persona impersonation, source verification, authenticated communications, and the evidentiary reliability of AI-generated media. Its central premise—that a convincing digital object should not be trusted merely because it appears authentic—has direct implications for courts, legal professionals, investigators, news organizations, and institutions increasingly required to distinguish genuine digital evidence from synthetic or manipulated content.

About the Authors

Mohammad Wazid

Mohammad Wazid is identified in the article as affiliated with the Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India. He is the corresponding author. The article’s CRediT authorship statement attributes to Wazid responsibility for conceptualization, methodology, investigation, formal analysis, data curation, supervision, visualization, validation, original drafting, and review and editing, indicating that he served as the principal research and writing contributor to the study

Amit Kumar Mishra

Amit Kumar Mishra is identified as affiliated with the Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, India. The article’s authorship statement credits him with resources supporting the research

Noor Mohd

Noor Mohd is identified as affiliated with the Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India. The article credits Noor Mohd with data curation

Ashok Kumar Das

Ashok Kumar Das is identified as affiliated with the Center for Security, Theory and Algorithmic Research at the International Institute of Information Technology, Hyderabad, India. The authorship statement credits him with validation. The article additionally discloses that Das was an editorial board member of Cyber Security and Applicationsbut did not participate in the editorial review or publication decision for this article.