RealSeal: Revolutionizing Media Authentication with Real-Time Realism Scoring

Authors: Bhaktipriya Radharapu, Harish Krishna

Published: 2024-11-26 18:48:23+00:00

Comment: Best Paper Award, Blue Sky Track at 26th ACM International Conference on Multimodal Interaction, Nov 2024, San Jose, Costa Rica

AI Summary

This paper introduces RealSeal, a novel paradigm for media authentication that advocates for watermarking real content at its source, rather than synthetic data. The approach uses multisensory inputs and machine learning to assess content realism in real-time. It proposes embedding a robust realism score within image metadata, aiming to fundamentally transform how digital media is trusted and circulated.

Abstract

The growing threat of deepfakes and manipulated media necessitates a radical rethinking of media authentication. Existing methods for watermarking synthetic data fall short, as they can be easily removed or altered, and current deepfake detection algorithms do not achieve perfect accuracy. Provenance techniques, which rely on metadata to verify content origin, fail to address the fundamental problem of staged or fake media. This paper introduces a groundbreaking paradigm shift in media authentication by advocating for the watermarking of real content at its source, as opposed to watermarking synthetic data. Our innovative approach employs multisensory inputs and machine learning to assess the realism of content in real-time and across different contexts. We propose embedding a robust realism score within the image metadata, fundamentally transforming how images are trusted and circulated. By combining established principles of human reasoning about reality, rooted in firmware and hardware security, with the sophisticated reasoning capabilities of contemporary machine learning systems, we develop a holistic approach that analyzes information from multiple perspectives. This ambitious, blue sky approach represents a significant leap forward in the field, pushing the boundaries of media authenticity and trust. By embracing cutting-edge advancements in technology and interdisciplinary research, we aim to establish a new standard for verifying the authenticity of digital media.


Key findings
The proposed source-based watermarking method significantly enhances media reliability and security by assessing authenticity during content creation, providing both provenance and a scene's realness credibility. Its numerous guardrails, including multisensory input and secure hardware/software integration, make it substantially more resistant to fakes than existing techniques. The system aims to establish a new universal standard for digital media authenticity, fostering greater trust across platforms.
Approach
The proposed method, RealSeal, involves three key steps: Sensing, Scoring, and Signing. Multisensory data (visual, 3D depth, audio, temporal motion, thermal) is captured in real-time, processed by a machine learning model to generate a realism score, and then cryptographically signed with the image metadata within a secure, tamper-proof hardware/OS environment.
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