Solutions to Deepfakes: Can Camera Hardware, Cryptography, and Deep Learning Verify Real Images?

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Authors: Alexander Vilesov, Yuan Tian, Nader Sehatbakhsh, Achuta Kadambi

Published: 2024-07-04 22:01:21+00:00

AI Summary

This paper explores methods for verifying the authenticity of images by distinguishing real images captured by camera hardware from synthetically generated images. It analyzes detection-based and cryptography-based approaches, highlighting their strengths and weaknesses, and proposing improvements for enhanced robustness.

Abstract

The exponential progress in generative AI poses serious implications for the credibility of all real images and videos. There will exist a point in the future where 1) digital content produced by generative AI will be indistinguishable from those created by cameras, 2) high-quality generative algorithms will be accessible to anyone, and 3) the ratio of all synthetic to real images will be large. It is imperative to establish methods that can separate real data from synthetic data with high confidence. We define real images as those that were produced by the camera hardware, capturing a real-world scene. Any synthetic generation of an image or alteration of a real image through generative AI or computer graphics techniques is labeled as a synthetic image. To this end, this document aims to: present known strategies in detection and cryptography that can be employed to verify which images are real, weight the strengths and weaknesses of these strategies, and suggest additional improvements to alleviate shortcomings.

Key findings

Detection-based methods alone are insufficient for long-term robustness due to the adversarial arms race between generators and detectors. Cryptography-based methods, combined with advanced camera sensors that can distinguish 3D scenes from 2D representations, offer a more promising and future-proof approach to verifying image authenticity. New file formats are suggested to improve searchability and user confidence in identifying real images.

Approach

The paper proposes a two-pronged approach: first, using cryptography (specifically PKI) to digitally sign images at the camera level, ensuring provenance; second, enhancing camera hardware with advanced sensors (like lidar and polarization sensors) to detect whether an image depicts a 3D scene, mitigating spoofing attempts.

Datasets

DFDC (DeepFake Detection Challenge) dataset is mentioned as an example dataset used in deepfake detection research, but is not explicitly used in the paper's main contribution.

Model(s)

The paper mentions various models used in deepfake detection research (e.g., EfficientNet, Vision Transformers), but it does not utilize or propose specific models for its main contribution. The focus is on cryptographic and hardware solutions.

Author countries

USA

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