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

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

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

Comment: Published in Foundations and Trends in Signal Processing (#1 in Signal Processing, #3 in Computer Science)

Journal Ref: Foundations and Trends in Signal Processing (2025)

AI Summary

This research explores zero-shot deepfake detection, an adaptive approach to identify manipulated content without prior exposure to specific deepfake variations. The paper reviews self-supervised learning, transformer-based classifiers, generative model fingerprinting, and meta-learning techniques for detection, alongside AI-driven prevention strategies such as adversarial perturbations, digital watermarking, real-time monitoring, and blockchain-based verification. It emphasizes the necessity of an integrated defense framework to address the evolving deepfake threat and outlines key challenges and future research directions.

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

Zero-shot deepfake detection, leveraging self-supervised learning, transformer-based architectures, and generative model fingerprinting, offers a highly adaptable solution against evolving synthetic media. However, significant challenges remain, including adversarial attacks, scalability issues, ethical dilemmas, and the absence of standardized evaluation benchmarks. The paper concludes that an integrated defense framework combining zero-shot detection with proactive prevention strategies (e.g., adversarial perturbations, digital watermarking, blockchain) and interdisciplinary collaboration is crucial to combat the rising deepfake threat effectively.

Approach

For detection, the authors review techniques including contrastive learning (SimCLR, MoCo, BYOL), autoencoder-based and one-class SVM anomaly detection, Out-of-Distribution (OOD) detection, and temporal anomaly detection, alongside generative model fingerprinting (pixel-level, frequency domain, and latent space analysis). For prevention, they discuss adversarial perturbations to disrupt deepfake generators, digital watermarking for content authentication, blockchain for secure content verification, and AI-powered real-time monitoring with federated learning.

Datasets

UNKNOWN

Model(s)

UNKNOWN

Author countries

India

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