International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 73 | Issue 6 | Year 2025 | Article Id. IJCTT-V73I6P112 | DOI : https://doi.org/10.14445/22312803/IJCTT-V73I6P112

The Deepfake Conundrum: Assessing Generative AI's Threat to Digital Reality and Proposing a Multi-Layered Defense Framework

Ketan Modi

Received Revised Accepted Published
02 May 2025 03 Jun 2025 20 Jun 2025 30 Jun 2025

Citation :

Ketan Modi, "The Deepfake Conundrum: Assessing Generative AI's Threat to Digital Reality and Proposing a Multi-Layered Defense Framework," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 6, pp. 97-103, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I6P112

Abstract

This research comprehensively investigates the escalating threat posed by generative AI-powered deepfakes, revealing critical vulnerabilities across digital ecosystems. Through rigorous experimentation and analysis, we discovered that modern diffusion models (e.g., Stable Diffusion, Imagen) have reduced deepfake generation time by an average of 89% compared to earlier GAN-based approaches, while simultaneously achieving unprecedented levels of photorealism. In controlled Turing tests using our custom DeepTrap2024 dataset (n=15,000 samples), deepfakes generated by hybrid transformer-diffusion architectures consistently deceived human evaluators at rates exceeding 92%. Security vulnerability assessments demonstrated alarming failure rates: 78% of commercially deployed facial recognition biometric systems were successfully breached using GAN-generated synthetic media, and CEO voice deepfakes bypassed corporate multi-factor authentication protocols in 89% of simulated attacks. Crucially, forensic analysis revealed that current state-of-the-art detection algorithms (including spectral analysis, rPPG, and CNN ensembles) suffered catastrophic failure rates (>85% false negatives) when confronted with deepfakes from latent diffusion models. These discoveries emerged through a novel tripartite methodology: 1) Adversarial testing across three benchmark datasets (FaceForensics++, DFDC, DeepTrap2024) comparing generation techniques; 2) Penetration testing on critical infrastructure (biometric access, financial verification, digital evidence chains); 3) Development and stress-testing of a prototype "NeuroPrint" detector. This research was urgently necessitated by documented global financial losses exceeding $2.5 billion attributed directly to deepfake-enabled fraud (FTC Report, 2024), escalating incidents of non-consensual intimate imagery (NCII), and demonstrable interference in democratic processes, such as the widespread dissemination of deepfake robocalls targeting voters during the 2024 electoral primaries. Our findings underscore that deepfakes represent not merely a content moderation challenge but a systemic threat to the foundational pillars of data integrity, identity authenticity, and security infrastructure in the digital age.

Keywords

Deepfake Detection, Generative AI Security, Data Integrity Threats, Digital Authenticity Infrastructure, Diffusion Model Forensics, Biometric Spoofing, Zero-Trust Verification, Synthetic Media Risks, AI Accountability, Content Provenance.

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