Cloak before it’s fake: a comprehensive review of deepfake prevention and a novel conceptual cloaking framework
TL;DR
A conceptual prevention framework that applies imperceptible, adaptive perturbations directly to images and video frames at the source to disrupt the feature extraction mechanisms of deepfake generation models, thereby hindering the creation of convincing synthetic media while preserving visual fidelity for human viewers.
Abstract
Abstract The rapid advancement of deepfake technologies, driven by generative artificial intelligence, has made distinguishing authentic media from synthetic content increasingly challenging. While much research has concentrated on detecting deepfakes after their creation, proactive prevention strategies remain relatively underdeveloped. This review provides a comprehensive examination of the evolution of deepfake generation pipelines, the risks they pose, and the current landscape of detection techniques. It also critically evaluates existing preventive measures such as digital watermarking, cryptographic signing, visual obfuscation, and adversarial image cloaking highlighting their shortcomings in terms of scalability, robustness, and practical implementation. To address these limitations, the paper introduces a conceptual prevention framework that applies imperceptible, adaptive perturbations directly to images and video frames at the source. These perturbations are designed to disrupt the feature extraction mechanisms of deepfake generation models, thereby hindering the creation of convincing synthetic media while preserving visual fidelity for human viewers. The proposed approach emphasizes resilience across different model architectures and suitability for real-time deployment in mobile and web-based environments. The study concludes by outlining future research directions to validate and refine this prevention strategy, advocating for a proactive shift in digital content protection that prioritizes prevention alongside detection.