Uncovering the invisible signature of AI washed intellectual property

When a piece of copyrighted code, a proprietary design, or a unique musical composition is fed into a Generative AI model for washing, the goal is to retain the core value and structure of the original work while subtly altering its metadata and stylistic features, enough to erase the ownership trail.

This algorithmic transformation creates a derivative work that is technically new yet functionally identical to the stolen IP.

For digital forensics and IP law, this constitues the single greatest challenge of the AI era; the traditional methods of proving theft, comparing files hashes or relying on easily removed metadata, are now completely obsolete.

The attribution gap

The legal challenge is complex, creating an attribution gap that spans the entire AI supply chain.

So, how does an attorney technically demonstrate that a piece of AI-generated code, is structurally based on stolen proprietary code, when the variables are renamed and the structure is slightly altered?

Even if theft is proven, who is the defendent? The user who provided the prompt? The model deployer? Or the model developer who potentially trained the algorithm on unlicensed data?

“Until more research on detecting such manipulation and identification is established with scientific rigor, it would always depend on expert testimony and counter expert testimony. But the hope is that it won't be that distant future that such scientifically rigorous methods will be established for acceptance by judiciary.” said Prof Sandeep K Shukla, Director, IIIT Hyderabad.

Beyond the hash

To overcome the failure of hash-based verification, forensic science has rapidly evolved into AI Lineage Tracing. The key insight is that while AI changes the file's surface, it leaves behind structural, functional, and even stylistic micro-patterns, the latent fingerprint, that point back to the original source.

The mechanisms of deep similarity analysis:

Modern AI-based forensic tools no longer look at the file as a string of data; they analyse the functional or conceptual patterns.

• For Code: Tools compare the underlying functional logic and API-call structure. If the stolen code and the "washed" version execute the same series of steps to achieve the same proprietary result, that is strong evidence of derivation, regardless of superficial syntax changes.

• For Art/Media: Forensics uses Diffusion Analysis to compare the output against a library of known originals. These systems check if the new output conceptually sits within the same conceptual "cluster" or "latent space" as the original work, even after being transformed.

“When someone alters code or artwork, the file’s hash value changes. Modern AI-based forensic tools do not rely on hashes alone. They compare the structure and visual or functional patterns of the new file with large libraries of known originals. These systems find close matches and this makes it possible to detect that a suspicious file is a modified version of an earlier work,” said Kaushal Bheda, Director at Pelorus Technologies.

The three pillars of court-ready evidence

Attorneys are now building their cases not on a single piece of evidence, but on a convergence of three core pillars that establish an irrefutable chain of evidence:

1. Deep similarity analysis: Technical evidence proving structural or functional equivalence between the 'washed' output and the original IP.

2. Temporal analysis: Proving that the claimed independent creation timeline of the defendant does not hold up, linking the appearance of the 'washed' work directly to the period following the original theft or exposure.

3. Training-data provenance: Investigating the AI model's supply chain to demonstrate that the algorithm was trained on the stolen, copyrighted material. This shifts liability potentially toward the model developer if the training was unlawful.

Regulation and Resilience

The battle for digital intellectual property is moving from firewalls to courtrooms, forcing a collision between cutting-edge technology and outdated law. For IP frameworks to remain relevant, they must adapt to recognise the invisible signature of algorithmic theft. This requires mandatory forensic audit trails for commercial AI, well-defined liability chains from data source to model output, and a legal system willing to accept technically complex evidence.

The AI arms race has forced forensics experts to become digital archaeologists, digging beneath the surface of the output to find the residual essence of the original work. By successfully linking the transformed output back to the original theft, the objective is clear: to finally shift liability onto those who knowingly modify and profit from the output of AI-washed crime.