TH-Bench: Evaluating Evading Attacks via Humanizing AI Text on Machine-Generated Text Detectors
This work addresses the problem of inconsistent benchmarking for evading attacks on text detectors, which is crucial for researchers and practitioners in AI security, though it is incremental as it builds on existing attacks and detectors.
The authors tackled the lack of a unified evaluation framework for evading attacks that humanize machine-generated text to bypass detectors, by introducing TH-Bench, which assesses 6 attacks against 13 detectors across 6 datasets and 19 domains, revealing no single attack excels across all dimensions and identifying trade-offs.
As Large Language Models (LLMs) advance, Machine-Generated Texts (MGTs) have become increasingly fluent, high-quality, and informative. Existing wide-range MGT detectors are designed to identify MGTs to prevent the spread of plagiarism and misinformation. However, adversaries attempt to humanize MGTs to evade detection (named evading attacks), which requires only minor modifications to bypass MGT detectors. Unfortunately, existing attacks generally lack a unified and comprehensive evaluation framework, as they are assessed using different experimental settings, model architectures, and datasets. To fill this gap, we introduce the Text-Humanization Benchmark (TH-Bench), the first comprehensive benchmark to evaluate evading attacks against MGT detectors. TH-Bench evaluates attacks across three key dimensions: evading effectiveness, text quality, and computational overhead. Our extensive experiments evaluate 6 state-of-the-art attacks against 13 MGT detectors across 6 datasets, spanning 19 domains and generated by 11 widely used LLMs. Our findings reveal that no single evading attack excels across all three dimensions. Through in-depth analysis, we highlight the strengths and limitations of different attacks. More importantly, we identify a trade-off among three dimensions and propose two optimization insights. Through preliminary experiments, we validate their correctness and effectiveness, offering potential directions for future research.