论文标题
验证码攻击：将验证码反对人类
Captcha Attack: Turning Captchas Against Humanity
论文作者
论文摘要
如今，人们在在线平台上生成和共享大量内容（例如，社交网络，博客）。 2021年，每日活跃的Facebook用户每分钟发布约1.5万张照片。内容主持人不断监视这些在线平台，以防止不适当的内容传播（例如，仇恨言论，裸体图像）。基于深度学习（DL）的进步，自动内容主持人（ACM）可帮助人类主持人处理高数据量。尽管具有优势，但攻击者仍可以利用DL组件的弱点（例如预处理，模型）来影响其性能。因此，攻击者可以通过逃避ACM来利用此类技术来传播不适当的内容。 在这项工作中，我们提出了Catpcha Attack（CAPA），这是一种对抗技术，允许用户通过逃避ACM控件在线传播不适当的文本。 CAPA，通过生成自定义文本验证码，利用ACM的粗心设计实现和内部程序漏洞。我们测试了对现实世界中ACM的攻击，结果证实了我们简单而有效的攻击的残酷性，在大多数情况下，逃避成功均达到了100％的逃避成功。同时，我们证明了设计CAPA缓解措施的困难，在Captchas Research领域开辟了新的挑战。
Nowadays, people generate and share massive content on online platforms (e.g., social networks, blogs). In 2021, the 1.9 billion daily active Facebook users posted around 150 thousand photos every minute. Content moderators constantly monitor these online platforms to prevent the spreading of inappropriate content (e.g., hate speech, nudity images). Based on deep learning (DL) advances, Automatic Content Moderators (ACM) help human moderators handle high data volume. Despite their advantages, attackers can exploit weaknesses of DL components (e.g., preprocessing, model) to affect their performance. Therefore, an attacker can leverage such techniques to spread inappropriate content by evading ACM. In this work, we propose CAPtcha Attack (CAPA), an adversarial technique that allows users to spread inappropriate text online by evading ACM controls. CAPA, by generating custom textual CAPTCHAs, exploits ACM's careless design implementations and internal procedures vulnerabilities. We test our attack on real-world ACM, and the results confirm the ferocity of our simple yet effective attack, reaching up to a 100% evasion success in most cases. At the same time, we demonstrate the difficulties in designing CAPA mitigations, opening new challenges in CAPTCHAs research area.
