The article also explores potential solutions to data poisoning, such as paying more attention to the source and usage of input data, ensemble modeling, and audits. It also discusses the wider implications of adversarial approaches, such as data poisoning, in the context of technological governance and the rights of artists and users.
Key takeaways:
- Data poisoning is a technique where an image's pixels are subtly altered to disrupt AI training, resulting in unpredictable and unintended results from text-to-image generators.
- The tool 'Nightshade' has been developed to empower artists and fight back against unauthorised image scraping by 'poisoning' the images.
- Proposed solutions to data poisoning include greater attention to data sources, ensemble modeling to detect outliers, and audits using a 'test battery' of hold-out data.
- Data poisoning is seen by some as an innovative solution to protect the rights of artists and users against the indiscriminate use of online data by tech companies.