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Attacking Only Half the Data: Two-Sided Evaluation of AI-Text Detector Robustness
We investigate the robustness of widely used AI text detectors under attacks applied to both human and machine text. Prior evaluations typically attack only machine text, overlooking that human text is routinely modified through grammar correction, formatting, or invisible character insertion. We in
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