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

• URL: http://arxiv.org/abs/2509.18461v1
• Date: Mon, 22 Sep 2025 22:33:16 GMT
• Title: 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,
• Abstract summary: Deepfake threats to digital security, media integrity, and public trust have increased rapidly.<n>This research explored zero-shot deepfake detection, an emerging method even when the models have never seen a particular deepfake variation.
• Score: 7.89029114152292
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning [50.79277723970418]
  imitation attacks can lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. We propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.
  arXiv  Detail & Related papers  (2024-08-27T07:26:10Z)
• Deepfake Media Forensics: State of the Art and Challenges Ahead [51.33414186878676]
  AI-generated synthetic media, also called Deepfakes, have influenced so many domains, from entertainment to cybersecurity. Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.
  arXiv  Detail & Related papers  (2024-08-01T08:57:47Z)
• Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges [40.11614155244292]
  This survey traces the evolution of deepfake detection from early single-modal methods to sophisticated multi-modal approaches.<n>We present a structured taxonomy of detection techniques and analyze the transition from GAN-based to diffusion model-driven deepfakes.
  arXiv  Detail & Related papers  (2024-06-11T05:48:04Z)
• Deepfake Sentry: Harnessing Ensemble Intelligence for Resilient Detection and Generalisation [0.8796261172196743]
  We propose a proactive and sustainable deepfake training augmentation solution. We employ a pool of autoencoders that mimic the effect of the artefacts introduced by the deepfake generator models. Experiments reveal that our proposed ensemble autoencoder-based data augmentation learning approach offers improvements in terms of generalisation.
  arXiv  Detail & Related papers  (2024-03-29T19:09:08Z)
• XAI-Based Detection of Adversarial Attacks on Deepfake Detectors [0.0]
  We introduce a novel methodology for identifying adversarial attacks on deepfake detectors using XAI. Our approach contributes not only to the detection of deepfakes but also enhances the understanding of possible adversarial attacks.
  arXiv  Detail & Related papers  (2024-03-05T13:25:30Z)
• NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection [50.33525966541906]
  Existing multimodal detection methods capture audio-visual inconsistencies to expose Deepfake videos. We propose a novel Deepfake detection method to mine the correlation between Non-critical Phonemes and Visemes, termed NPVForensics. Our model can be easily adapted to the downstream Deepfake datasets with fine-tuning.
  arXiv  Detail & Related papers  (2023-06-12T06:06:05Z)
• Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques [1.0718756132502771]
  adversarial examples are subtle perturbations artfully injected into clean images or videos. Deepfakes have emerged as a potent tool to manipulate public opinion and tarnish the reputations of public figures. This article delves into the multifaceted world of adversarial examples, elucidating the underlying principles behind their capacity to deceive deep learning algorithms.
  arXiv  Detail & Related papers  (2023-02-22T23:48:19Z)
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data [64.65952078807086]
  Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs) Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. We seek a proactive and sustainable solution on deepfake detection by introducing artificial fingerprints into the models.
  arXiv  Detail & Related papers  (2020-07-16T16:49:55Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.