Top detectors in the shared task achieved above 99% true positive rate at 5% false positive rate on the RAID benchmark when all domains and models were seen during training.
A Mutation-based Text Generation for Adversarial Machine Learning Applications
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abstract
Many natural language related applications involve text generation, created by humans or machines. While in many of those applications machines support humans, yet in few others, (e.g. adversarial machine learning, social bots and trolls) machines try to impersonate humans. In this scope, we proposed and evaluated several mutation-based text generation approaches. Unlike machine-based generated text, mutation-based generated text needs human text samples as inputs. We showed examples of mutation operators but this work can be extended in many aspects such as proposing new text-based mutation operators based on the nature of the application.
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cs.CL 1years
2025 1verdicts
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GenAI Content Detection Task 3: Cross-Domain Machine-Generated Text Detection Challenge
Top detectors in the shared task achieved above 99% true positive rate at 5% false positive rate on the RAID benchmark when all domains and models were seen during training.