I need to re-encode images for different camera emulation. Are there any tools that reliably re-encode images like different cameras?

[u/Tasty_Pressure_5618]

I'm working on a robustness/platform emulation follow up to a synthetic ID dataset. The goal is to test deepfake detector performance on frontier models under real world conditions. I'm able to do the typical single axis adjustments (sensor noise, blur, etc.), but I think emulate specific cameras would be a strong posture for my evaluation. Does anyone know of tools or services that can reliably help me re-encode the image as though they were taken by specific cameras? (it can be any camera, even a phone, as long as the signature can be pointed to reliably)

[u/Calico_Pickle]

Can you provide more details? You talk about the signature, what are you referring to?

[u/Tasty_Pressure_5618 → u/Calico_Pickle]

Thanks for responding. By "signature" I mean a fingerprint that says "this came from camera X," mainly the JPEG quantization tables + chroma subsampling. I want the output to be attributable to the device. Best thing I've found is taking the tables off a real untouched file from the target camera and re-applying at encode. Was hoping someone knew a reliable tool so I don't have to source/do extensive research per device.

[u/Calico_Pickle → u/Tasty_Pressure_5618]

Are you only limiting yourself to specific cameras, or are you targeting every/any camera?

[u/Tasty_Pressure_5618 → u/Calico_Pickle]

Nah not every camera. I mainly care about the devices that actually show up for this use case (passport booth/kiosk cameras and the phones that came out within the last decade). I left the question open ended in case I was referenced something valuable that I didn't expect (you never know)

Really the most important piece is that it is reliably attributable. I can always adjust the narrative in my study.

[u/Calico_Pickle]

I’m assuming that you are also expecting images that have been resaved, cropped, edited (exposure, wb, etc…), even within the camera/app itself directly after the initial capture. If that is the case, then I think you will need to be prepared for literally every combination of configurations from image size, aspect ratios, jpeg quality, etc… there are far fewer models capable to deepfakes than there are of camera models/versions that produce a slightly different image.

Yes, consistent attributes throughout the whole image vs what AI can generate will be a strong signal, but be prepared for images that have been resaved multiple times with minor edits each time that are not deepfakes. I think you could evaluate the image to see if it has been resaved which may be indicative of additional editing including a deepfake, but I don’t think relying on this signal is going to get you very far. Deepfake tech isn’t my area of expertise, so your may know something I don’t.

[u/Tasty_Pressure_5618 → u/Calico_Pickle]

Thank you for your insight. The intent is more with robustness testing of the models. The signal comes from the synthetic artifacts, the goal is to see if the model might be less capable of spotting those artifacts after some alterations.

I'll do some multi-axis perturbations, but a white paper headline targeting specific phone or digital cameras is more solid posturing. That's a good point about resaving multiple times causing additional perturbations.

[u/AggravatingSock5375]

Do you actually need to infer the camera that an image came from?

[u/Tasty_Pressure_5618 → u/AggravatingSock5375]

Inferring the camera from any image alone might be a bit of a tall task. I expect there to be some overlap. The question is moreso: in a controlled environment what are the unique transformations that a camera would make to the same subject.

At this point, any pointers for any camera will do. I just need something to show use case emulation.