The Statistical Footprint of AI-Generated Images: Difference between revisions
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=== Research Question and Project Goals === | === Research Question and Project Goals === | ||
For the general public, the inability to distinguish real from synthetic | For the general public, the inability to distinguish real from synthetic raises concerns about misinformation, as well as a loss of creativity intrinsic to humans. However, for Image Systems Engineering, artificially generated images also provoke a serious question of engineering safety. Developers of autonomous systems, such as autonomous vehicles (AVs), are increasingly turning to generative AI to create training data for perception systems to bridge the gap between expensive real-world data and the need for massive edge-case datasets, such as car accidents and severe weather. Although these digital twins may appear realistic to human observers, they often fail to behave like real sensors, lacking the correct noise, spectral, or optical properties. Thus, artificially generated data introduces a high-risk domain gap where autonomous systems are trained on hallucinations rather than physics. This poses a critical question: '''Can we discern the real from the artificial?''' | ||
This project conducts a forensic analysis of AI-generated sensor data to determine its viability for simulation. We ignore the ''semantic'' content (e.g., whether the car looks like a car) and focus entirely on the ''physical'' statistics (how the image was formed). By comparing a '''Physical Ground Truth''' (simulated via [[ISETCam]]) against a '''Generative Reconstruction''' (generated via Stable Diffusion v1.5), we aim to quantify the statistical "fingerprints" of the AI across four domains: | This project conducts a forensic analysis of AI-generated sensor data to determine its viability for simulation. We ignore the ''semantic'' content (e.g., whether the car looks like a car) and focus entirely on the ''physical'' statistics (how the image was formed). By comparing a '''Physical Ground Truth''' (simulated via [[ISETCam]]) against a '''Generative Reconstruction''' (generated via Stable Diffusion v1.5), we aim to quantify the statistical "fingerprints" of the AI across four domains: | ||
Revision as of 21:12, 13 December 2025
Introduction
The Blur Between Real and Artificial
In 2023, the Sony World Photography Awards awarded a prize in the Creative category to an image titled "The Electrician". The image presented a haunting, black-and-white portrait of two women. However, the artist, Boris Eldagsen, refused the award, revealing that the work was not a photograph at all, but a synthetic creation generated by AI. This event marked a significant turning point: synthetic imagery had crossed a threshold of fidelity where even expert judges could no longer distinguish pixels captured by photons from artificially generated pixels.[1] This blurring of reality is compounded by the unprecedented scale of production. Recent reports indicate that in just 1.5 years, generative AI models have produced as many images as traditional photography produced in its first 150 years (approximately 15 billion images).[2] We are rapidly entering an era where a significant portion of digital visual data is artificially generated, rather than captured on a physical image sensing system.
Research Question and Project Goals
For the general public, the inability to distinguish real from synthetic raises concerns about misinformation, as well as a loss of creativity intrinsic to humans. However, for Image Systems Engineering, artificially generated images also provoke a serious question of engineering safety. Developers of autonomous systems, such as autonomous vehicles (AVs), are increasingly turning to generative AI to create training data for perception systems to bridge the gap between expensive real-world data and the need for massive edge-case datasets, such as car accidents and severe weather. Although these digital twins may appear realistic to human observers, they often fail to behave like real sensors, lacking the correct noise, spectral, or optical properties. Thus, artificially generated data introduces a high-risk domain gap where autonomous systems are trained on hallucinations rather than physics. This poses a critical question: Can we discern the real from the artificial?
This project conducts a forensic analysis of AI-generated sensor data to determine its viability for simulation. We ignore the semantic content (e.g., whether the car looks like a car) and focus entirely on the physical statistics (how the image was formed). By comparing a Physical Ground Truth (simulated via ISETCam) against a Generative Reconstruction (generated via Stable Diffusion v1.5), we aim to quantify the statistical "fingerprints" of the AI across four domains:
- Spatial Statistics: Texture and frequency distribution.
- Photometric Statistics: Signal-dependent noise response.
- Spectral Statistics: Inter-channel color correlation.
- Optical Statistics: Point spread function (PSF) and diffraction.