Github Vinusankars Reliability Of Ai Text Detectors Can Ai Generated Text Be Reliably Detected

GitHub - Vinusankars/Reliability-of-AI-text-detectors: Can AI-Generated Text Be Reliably Detected?

GitHub - Vinusankars/Reliability-of-AI-text-detectors: Can AI-Generated Text Be Reliably Detected? Consequently, the reliable detection of ai generated text has become a critical area of research. ai text detectors have shown to be effective under their specific settings. in this paper, we stress test the robustness of these ai text detectors in the presence of an attacker. I am an ai research scientist at meta, working with the llama safety team in the menlo park office. i graduated with a phd in computer science from the university of maryland, college park. my phd thesis is on ai safety, advised by prof. soheil feizi. i am a recipient of the kulkarni fellowship 2023.

Are AI-Generated Text Detectors Robust To Adversarial Perturbations? | Guanhua Huang

Are AI-Generated Text Detectors Robust To Adversarial Perturbations? | Guanhua Huang Recent works attempt to tackle this problem either using certain model signatures present in the generated text outputs or by applying watermarking techniques that imprint specific patterns onto them. in this paper, we show that these detectors are not reliable in practical scenarios. In this paper, both empirically and theoretically, we show that these detectors are not reliable in practical scenarios. In a recent paper, researchers vinu sankar sadasivan, aounon kumar, sriram balasubramanian, wenxiao wang, and soheil feizi analyzed the reliability of current ai generated text detectors. they demonstrated both empirically and theoretically that these detectors are not reliable in practical scenarios. In particular, we develop a recursive paraphrasing attack to apply on ai text, which can break a whole range of detectors, including the ones using the watermarking schemes as well as neural network based detectors, zero shot classifiers, and retrieval based detectors.

GitHub - Walidnk/ai-generated-text-detector: A Deep Learning Model That Check If A Text Is Or ...

GitHub - Walidnk/ai-generated-text-detector: A Deep Learning Model That Check If A Text Is Or ... In a recent paper, researchers vinu sankar sadasivan, aounon kumar, sriram balasubramanian, wenxiao wang, and soheil feizi analyzed the reliability of current ai generated text detectors. they demonstrated both empirically and theoretically that these detectors are not reliable in practical scenarios. In particular, we develop a recursive paraphrasing attack to apply on ai text, which can break a whole range of detectors, including the ones using the watermarking schemes as well as neural network based detectors, zero shot classifiers, and retrieval based detectors. We introduce recursive paraphrasing attack to stress test a wide range of detection schemes, including the ones using the watermarking as well as neural network based detectors, zero shot classifiers, and retrieval based detectors. Recent works attempt to tackle this problem either using certain model signatures present in the generated text outputs or by applying watermarking techniques that imprint specific patterns onto them. in this paper, we show that these detectors are not reliable in practical scenarios. Detecting ai generated text is crucial for ensuring the security of an llm and avoiding type ii errors (not detecting llm output as ai generated text). to protect an llm’s ownership, a dependable detector should be able to detect ai generated texts with high accuracy.

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