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Commercial incentives have further distorted this landscape -- detection services and \"de-AIification\" tools often operate within the same supply chain, replacing evaluation of content quality with judgment of content origin. We present StyleShield, the first flow matching framework for conditional text style transfer, operating directly in continuous token embedding space via a DiT backbone with zero-initialized cross-attention adapters conditioned on frozen Qwen-7B representations. At inference, we adapt the SDEdit paradigm from image synthesis to text embeddings, with a single parameter gamma pro","title":"StyleShield: Exposing the Fragility of AIGC Detectors through Continuous Controllable Style Transfer","url":"https://arxiv.org/abs/2605.00924","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.00924v2 Announce Type: replace-cross \nAbstract: AI-generated content (AIGC) detectors are increasingly deployed in high-stakes settings such as academic integrity screening, yet their reliability rests on a fundamental paradox: as language models are trained on human-written corpora, the statistical boundary between AI and human writing will inevitably dissolve as models improve. Commercial incentives have further distorted this landscape -- detection services and \"de-AIification\" tools often operate within the same supply chain, replacing evaluation of content quality with judgment of content origin. 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