Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?

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Authors: Ayan Sar, Sampurna Roy, Tanupriya Choudhury, Ajith Abraham

Published: 2025-09-22 22:33:16+00:00

AI Summary

This research explores zero-shot deepfake detection, aiming to identify fake content without prior exposure to specific deepfake variations. It investigates self-supervised learning, transformer-based classifiers, generative model fingerprinting, and prevention strategies like adversarial perturbations and digital watermarking.

Abstract

Generative adversarial networks (GANs) and diffusion models have dramatically advanced deepfake technology, and its threats to digital security, media integrity, and public trust have increased rapidly. This research explored zero-shot deepfake detection, an emerging method even when the models have never seen a particular deepfake variation. In this work, we studied self-supervised learning, transformer-based zero-shot classifier, generative model fingerprinting, and meta-learning techniques that better adapt to the ever-evolving deepfake threat. In addition, we suggested AI-driven prevention strategies that mitigated the underlying generation pipeline of the deepfakes before they occurred. They consisted of adversarial perturbations for creating deepfake generators, digital watermarking for content authenticity verification, real-time AI monitoring for content creation pipelines, and blockchain-based content verification frameworks. Despite these advancements, zero-shot detection and prevention faced critical challenges such as adversarial attacks, scalability constraints, ethical dilemmas, and the absence of standardized evaluation benchmarks. These limitations were addressed by discussing future research directions on explainable AI for deepfake detection, multimodal fusion based on image, audio, and text analysis, quantum AI for enhanced security, and federated learning for privacy-preserving deepfake detection. This further highlighted the need for an integrated defense framework for digital authenticity that utilized zero-shot learning in combination with preventive deepfake mechanisms. Finally, we highlighted the important role of interdisciplinary collaboration between AI researchers, cybersecurity experts, and policymakers to create resilient defenses against the rising tide of deepfake attacks.

Key findings

While zero-shot deepfake detection and prevention show promise, significant challenges remain, including generalization to new deepfake techniques, susceptibility to adversarial attacks, and computational limitations. The paper highlights the need for explainable AI, multimodal fusion, and quantum AI for improved deepfake defense.

Approach

The paper explores several zero-shot deepfake detection approaches: self-supervised learning to identify deviations from real-world data, transformer-based classifiers using textual prompts, and generative model fingerprinting to identify unique artifacts in synthetic media. Prevention strategies include adversarial perturbations to disrupt deepfake generators and digital watermarking for content authentication.

Datasets

FaceForensics++, Celeb-DF, DFDC (Deepfake Detection Challenge), DeeperForensics-1.0, FF++, and others are mentioned but not explicitly stated as used in the main contribution.

Model(s)

ResNet50, Vision Transformer (ViT), CLIP, Autoencoders, One-Class SVMs, GANs (StyleGAN, ProGAN, etc.), and others are mentioned but not explicitly stated as the core models in the main contribution.

Author countries

India, India, India, India

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