4 citations · 7 across the 4 of their papers we have counts for
5 papers
We Should Separate Memorization from Copyright
Adi Haviv, Niva Elkin-Koren, Uri Hacohen +2
The widespread use of foundation models has introduced a new risk factor of copyright issue. This issue is leading to an active, lively and on-going debate amongst the data-science…
CARLoS: Retrieval via Concise Assessment Representation of LoRAs at Scale
Shahar Sarfaty, Adi Haviv, Uri Hacohen +3
The rapid proliferation of generative components, such as LoRAs, has created a vast but unstructured ecosystem. Existing discovery methods depend on unreliable user descriptions or…
Not Every Image is Worth a Thousand Words: Quantifying Originality in Stable Diffusion
Adi Haviv, Shahar Sarfaty, Uri Hacohen +3
This work addresses the challenge of quantifying originality in text-to-image (T2I) generative diffusion models, with a focus on copyright originality. We begin by evaluating T2I m…
Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes
Uri Hacohen, Adi Haviv, Shahar Sarfaty +4
The advent of Generative Artificial Intelligence (GenAI) models, including GitHub Copilot, OpenAI GPT, and Stable Diffusion, has revolutionized content creation, enabling non-profe…
Can Copyright be Reduced to Privacy?
Niva Elkin-Koren, Uri Hacohen, Roi Livni +1
There is a growing concern that generative AI models will generate outputs closely resembling the copyrighted materials for which they are trained. This worry has intensified as th…