Authors: Bhaktipriya Radharapu, Harish Krishna
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.
Source: http://arxiv.org/abs/2411.17684v1
