I'm Rijal, an independent researcher from Indonesia. For the past few months, I've been developing a conceptual framework to proactively defend images and videos against generative AI manipulation.
The core idea, which I call "Adaptive Spider Web," is a multi-layered defense embedded at the point of capture:
1. *A "Structural Lie" Noise:* A pattern designed to disrupt an AI's understanding of 3D geometry, causing manipulation attempts to fail or produce illogical results.
2. *A Tamper-Evident Web Structure:* An adaptive, invisible web that acts as a digital seal.
3. *Reality Detection:* Conceptual methods to counter re-photography attacks (detecting moiré patterns, lighting anomalies, and biological signatures).
4. *Cryptographic Signature:* A final mathematical seal to ensure data integrity.
This is currently a conceptual project, and my background is in research and strategic analysis, not programming. I'm publishing this to share the idea and get feedback from a technical community like this one.
The full documentation is in the GitHub repository linked above. All thoughts, critiques, and ideas are welcome.
Thank you.
compressedgas•1h ago
#2 is called "watermarking". If the watermarking is done for each recipient differently, then it is called "traitor tracing".
#1 and #2 will eventually be defeated by newer models as they get trained on the protected images or need to pass some output condition. You should know that these so-called protective image distortions are the kind of thing that the generative adversary in GAN training learns to do and classifier learns to ignore.
Only #2 and #4, watermarking and signing are likely to be continue to be effective as models improve. For content signing there is the C2PA specification https://c2pa.org/specifications/specifications/2.2/index.htm... that describes how to include signatures in most media types.
rijal028•1h ago
Thank you for this incredibly valuable and insightful feedback. You've perfectly articulated the core challenges and long-term viability of the different defensive layers.
I completely agree that the "AI Poison" layer is likely a temporary defense. Your validation of watermarking and especially cryptographic signing as the most robust long-term solutions is a crucial takeaway for me.
And thank you especially for pointing me to the C2PA specification. I was not aware of this standard, and aligning my concept with it is a critical next step. I will be studying this document carefully.
This is exactly the kind of expert feedback I was hoping to get by sharing my work here. I truly appreciate you taking the time.
rijal028•3h ago
I'm Rijal, an independent researcher from Indonesia. For the past few months, I've been developing a conceptual framework to proactively defend images and videos against generative AI manipulation.
The core idea, which I call "Adaptive Spider Web," is a multi-layered defense embedded at the point of capture:
1. *A "Structural Lie" Noise:* A pattern designed to disrupt an AI's understanding of 3D geometry, causing manipulation attempts to fail or produce illogical results.
2. *A Tamper-Evident Web Structure:* An adaptive, invisible web that acts as a digital seal.
3. *Reality Detection:* Conceptual methods to counter re-photography attacks (detecting moiré patterns, lighting anomalies, and biological signatures).
4. *Cryptographic Signature:* A final mathematical seal to ensure data integrity.
This is currently a conceptual project, and my background is in research and strategic analysis, not programming. I'm publishing this to share the idea and get feedback from a technical community like this one.
The full documentation is in the GitHub repository linked above. All thoughts, critiques, and ideas are welcome.
Thank you.
compressedgas•1h ago
#1 and #2 will eventually be defeated by newer models as they get trained on the protected images or need to pass some output condition. You should know that these so-called protective image distortions are the kind of thing that the generative adversary in GAN training learns to do and classifier learns to ignore.
Only #2 and #4, watermarking and signing are likely to be continue to be effective as models improve. For content signing there is the C2PA specification https://c2pa.org/specifications/specifications/2.2/index.htm... that describes how to include signatures in most media types.
rijal028•1h ago
I completely agree that the "AI Poison" layer is likely a temporary defense. Your validation of watermarking and especially cryptographic signing as the most robust long-term solutions is a crucial takeaway for me.
And thank you especially for pointing me to the C2PA specification. I was not aware of this standard, and aligning my concept with it is a critical next step. I will be studying this document carefully.
This is exactly the kind of expert feedback I was hoping to get by sharing my work here. I truly appreciate you taking the time.