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| === The Blur Between Real and Artificial ===
| | #REDIRECT [[The Statistical Fingerprint of AI-Generated Images]] |
| [[File:Sony_Awards_The_Electrician.png|thumb|right|300px|Figure 1: "The Electrician" by Boris Eldagsen, the AI-generated image that won the Creative category at the 2023 Sony World Photography Awards.]] | |
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| 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.[[#ref1|<sup>[1]</sup>]] 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).[[#ref2|<sup>[2]</sup>]] 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.
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| [[File:AI_Generation_Statistics.png|thumb|right|300px|Figure 2: Statistics illustrating the scale of AI image generation distributed across different models.]]
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| === Research Question and Project Goals ===
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| 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?'''
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| This project conducts a forensic analysis to determine whether AI-generated images can be distinguished from camera-simulated images based on radiometric statistics. In this investigation, we ignore the semantic content (whether the car looks like a car) and focus entirely on the physical statistics (how the image was formed from the radiance source). By comparing a '''physical radiance''' dataset (ISET3D physically based ray tracing[[#ref3|<sup>[3]</sup>]]) and '''camera photograph''' dataset (ISETCam[[#ref4|<sup>[4]</sup>]]) against an '''artificially generated image''' dataset (Stable Diffusion v1.5[[#ref5|<sup>[5]</sup>]]), we aim to quantify the statistical fingerprints of the AI across four domains:
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| * '''Spatial Statistics:''' Texture and frequency distribution.
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| * '''Photometric Statistics:''' Signal-dependent noise response.
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| * '''Spectral Statistics:''' Inter-channel color correlation.
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| * '''Optical Statistics:''' Point spread function (PSF) and diffraction.
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| == References ==
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| <div id="ref1">[1] Paul Glynn, "Sony World Photography Award 2023: Winner refuses award after revealing AI creation," *BBC News*, 18 April 2023.</div>
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| <div id="ref2">[2] Everypixel Journal, "AI Image Statistics Report," August 2023.</div>
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| <div id="ref3">[3] Wandell, B. and Liu, Z. (2024). "ISET HDR Driving scene light groups." *Stanford Digital Repository*.</div>
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| <div id="ref4">[4] Farrell, J. E., et al. (2012). "ISET: The Image Systems Engineering Toolbox." *Proceedings of the Society for Imaging Science and Technology*.</div>
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| <div id="ref5">[5] Rombach, R., et al. (2022). "High-Resolution Image Synthesis with Latent Diffusion Models." *CVPR*.</div>
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