GitMylo/Qwen2.5-Omni-7B-abliterated-gguf
8B • Updated • 285 • 1
TimeLordRaps
TimeLordRaps
AI & ML interests
Music, Methods, and Madness
Recent Activity
liked a model 2 days ago
GitMylo/Qwen2.5-Omni-7B-abliterated-gguf liked a model 2 days ago
GitMylo/Qwen2.5-Omni-7B-abliterated liked a model 2 days ago
netflix/void-modelOrganizations
None yet
liked 3
models 2 days ago
liked 2
models 3 days ago
commented a
paper 3 days ago
reactedto Severian's post with 👀 22 days ago
Post
430
New Technique to Deeply Poison AI on Images and Prove Creative Provenance
I've developed a new method to protect creative work from unauthorized AI training. My Poisonous Shield for Images algorithm embeds a deep, removal-resistant poison into the mathematical structure of your images. It's designed to be toxic to machine learning models, achieving up to 20-348% disruption in AI training convergence in benchmark tests.
Unlike traditional watermarks, this protection survives compression and resizing and is not removed by standard tools. The technique also embeds cryptographic proof of provenance directly into the image, verifying ownership and detecting tampering.
You can see examples and learn more about how and WHY it works better than current methods:
https://severian-poisonous-shield-for-images.static.hf.space
If you are interested in using this technology to protect your work from AI training and unauthorized use, please reach out to me. It is currently in the prototype phase but fully functioning and effective. Still working on expanding it to a production-grade usable app.
This is not intended as a pure self-promotion post. I am genuinely wanting to help creators and want to gauge interest from different communities. I've spent the past year and a half building this from scratch with new math and code to try and solve this massive problem.
I've developed a new method to protect creative work from unauthorized AI training. My Poisonous Shield for Images algorithm embeds a deep, removal-resistant poison into the mathematical structure of your images. It's designed to be toxic to machine learning models, achieving up to 20-348% disruption in AI training convergence in benchmark tests.
Unlike traditional watermarks, this protection survives compression and resizing and is not removed by standard tools. The technique also embeds cryptographic proof of provenance directly into the image, verifying ownership and detecting tampering.
You can see examples and learn more about how and WHY it works better than current methods:
https://severian-poisonous-shield-for-images.static.hf.space
If you are interested in using this technology to protect your work from AI training and unauthorized use, please reach out to me. It is currently in the prototype phase but fully functioning and effective. Still working on expanding it to a production-grade usable app.
This is not intended as a pure self-promotion post. I am genuinely wanting to help creators and want to gauge interest from different communities. I've spent the past year and a half building this from scratch with new math and code to try and solve this massive problem.
Hey bud,
1
#1 opened 26 days ago
by TimeLordRaps
updated a
Space 28 days ago
Akicou/MiniMax-M2-5-REAP-19
33
#1847 opened about 2 months ago
by TimeLordRaps
published a
Space 29 days ago
upvoted 2
papers about 2 months ago
liked a
model 2 months ago
upvoted 3
papers 2 months ago
Routing the Lottery: Adaptive Subnetworks for Heterogeneous Data
Paper • 2601.22141 • Published • 3
TTCS: Test-Time Curriculum Synthesis for Self-Evolving
Paper • 2601.22628 • Published • 35
ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas
Paper • 2601.21558 • Published • 60
liked a
model 2 months ago
liked 2
models 3 months ago
