Consumer devices now ship with generative image editing tools that can remove objects, move subjects, and create new visual content directly on phones, tablets, and laptops. Yet current benchmarks largely miss this setting: real on-device edits produced by black-box consumer generative AI editing tools, with variation across platforms, devices, edit operations, and post-edit compression. We introduce ConsumerGenEdit, a large-scale multilingual benchmark for language-guided localization of on-device generative image edits. The dataset contains 4,000 royalty-free source images and 48,000 manually edited images created on Apple, Google, and Samsung devices, covering object removal, object movement, and sketch-based object generation. To study robustness under realistic sharing conditions, we add 144,000 learned-compression variants at three quality levels. Each example includes platform, device, editing operation, compression metadata, bounding-box annotations, and edit descriptions in English, Bangla, and Persian/Farsi generated with five vision-language models (VLMs). We evaluate five separate benchmark VLMs on edit detection and edit-operation classification. By focusing on where and how consumer AI edits occur, rather than only whether an image is synthetic, ConsumerGenEdit provides a practical testbed for multilingual VLM grounding, image provenance, and robust interpretation of AI-edited media.

Generative AI writing assistants are now integrated into consumer platforms such as Apple Intelligence and Microsoft Copilot, enabling millions of users to automatically rewrite and stylize their text. While positioned as productivity tools, their deployment at scale introduces important and underexplored implications for privacy and platform safety. This paper examines the dual-use nature of platform-level text stylization. Stylization can enhance privacy by suppressing stylistic signals used for profiling and personal data inference. However, the same transformations can be leveraged to evade automated safeguards, including misinformation detection systems. We conduct empirical case studies on emotion inference and misinformation detection across benchmark datasets using deployed stylization modes. We evaluate downstream impact with fine-tuned open-source models and GPT-4o in a zero-shot setting. Our results show that stylization reduces emotion inference accuracy, lowering profiling risk, while increasing error rates in misinformation detection. This discovery reveal a measurable trade-off among privacy protection, moderation robustness, and stylization, highlighting new design and governance challenges for industry deployment.