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hello thank you so much for joining this talk my name is Sahar and Iamp;#39;m going to present our paper adversarial watermarking Transformer towards tracing text provenance with data hiding this work is done in collaboration with Mario Fritz recent language models such as gpt2 have shown a great performance with high quality output text they were able to generate entire articles that seemed very convincing for human evaluators an even more powerful and larger model was later released but this raised docHub concerns about the implication and potential abuse of such models as a countermeasure these models were released in a state release or only made available through a commercial Black Box API active and protective release strategies are still lacking current passive defenses such as classifiers depend on the generation configuration in the language model which is not sustainable in the long run therefore we study language watermarking as an active and sustainable solution for Ge
