发表机构
PeopleTec, Inc.（PeopleTec公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究传统验证码面临的问题，提出以GPU渲染时间作为抗人工智能的验证码信号，通过被动部署刻画对手，收集样本并实验，发现软件渲染自动化与真实GPU在渲染时间上有差异，无头自动化有独特定时特征。
AI 中文摘要
传统验证码所带来的谜题正逐渐被现代人工智能系统破解，而行为和加密认证防御会带来隐私或注册成本。我们研究了一种正交信号：在可控的WebGL渲染工作负载下客户端GPU的物理定时行为。与将像素输出哈希成静态设备标识符的WebGL指纹识别不同，我们测量渲染时间动态来进行分类而非识别，不会泄露持久标识符。我们通过12小时的被动部署来刻画实际中的对手（207个未经请求的请求；86%是自动化的；85%声称使用浏览器的客户端未能通过HTTP头一致性检查）。然后通过真实浏览器使用的单个公共端点（正类，13个不同的GPU）以及跨渲染后端矩阵的键控无头自动化（负类）收集带标签的GPU定时样本。软件渲染的自动化（根据经验是实际中占主导的对手）在平均渲染时间上与真正的GPU相差约5倍。在一个混淆控制的比较中（相同的GPU系列和浏览器引擎，仅在无头与交互式执行上不同），真实硬件上的无头自动化仍然表现出独特的定时特征，在帧抖动、定时器量化率和变异系数上与人类样本相差75 - 106%。我们将这些作为关于单个GPU架构的试点规模发现进行报告，并概述建立通用性所需的跨架构收集。
英文摘要
Conventional CAPTCHAs pose puzzles that modern AI systems increasingly solve, while behavioral and cryptographic-attestation defenses carry privacy or enrollment costs. We investigate an orthogonal signal: the physical timing behavior of a client's GPU under a controlled WebGL rendering workload. Unlike WebGL fingerprinting, which hashes pixel output into a static device identifier, we measure render-timing dynamics to classify rather than identify, leaking no persistent identifier. We characterize the in-the-wild adversary with a 12-hour passive deployment (207 unsolicited requests; 86% automated; 85% of browser-claiming clients failed HTTP header-consistency checks). We then collect labeled GPU-timing samples through a single public endpoint exercised by real browsers (positive class, 13 distinct GPUs) and by keyed headless automation across a render-backend matrix (negative class). Software-rendered automation -- empirically the dominant real-world adversary -- separates from genuine GPUs by roughly 5x in mean render time. On a confound-controlled comparison (identical GPU family and browser engine, differing only in headless vs. interactive execution), headless automation on real hardware still exhibits a distinct timing signature, separating from human samples by 75-106% on frame jitter, timer-quantization ratio, and coefficient of variation. We report these as pilot-scale findings on a single GPU architecture and outline the cross-architecture collection required to establish generalization.
