隐写战场：评估图像隐写术攻击与隐写分析防御
Stego Battlefield: Evaluating Image Steganography Attacks and Steganalysis Defenses
AI总结：
本文提出SADBench基准，系统评估隐写攻击能力和隐写分析防御能力，揭示攻击稳定性、检测成本及转移性差异，揭示现实威胁在社交媒体中持续存在。
摘要：
图像隐写术广泛用于保护用户隐私和实现隐蔽通信。然而，它也可以被对手滥用作为隐蔽通道，绕过内容审查，传播有害语义，甚至在图像中隐藏恶意指令以引发大模型危险输出，构成持续演化的实际安全风险。为解决缺乏统一系统评估框架的问题，我们提出SADBench，一个系统基准，评估对手通过隐写术注入有害信息的能力以及防御方通过隐写分析检测此类威胁的能力。关键在于SADBench包含4个核心任务：隐写攻击能力评估、隐写分析防御能力评估、效率评估和转移性评估。它在多样化的覆盖分布上评估图像-负载和文本-负载隐写术，利用有害视觉语义和有毒指令模拟恶意攻击。在广泛攻击和检测器集合上，SADBench揭示：(i) INN和自动编码器方法相比其他架构表现更稳定；(ii) 领域内检测几乎完美且成本更低；(iii) 存在关键的转移性不对称，攻击能稳健泛化到新分布，而检测器无法适应；(iv) 现实威胁在社交媒体中持续存在，其中负载要么能承受最小压缩，或通过模拟训练有效适应攻击压缩。总体而言，SADBench建立了一个系统、可重复和可扩展的框架来量化风险，为隐写防御的可衡量和安全驱动进步铺平道路。
英文摘要：
Image steganography is widely used to protect user privacy and enable covert communication. However, it can also be abused by the adversary as a covert channel to bypass content moderation, disseminate harmful semantics, and even hide malicious instructions in images to elicit dangerous outputs from large models, posing a practical security risk that continues to evolve. To address the lack of a unified and systematic evaluation framework, we propose SADBench, a systematic benchmark that assesses the adversary's ability to inject harmful secrets via steganography and the defender's ability to detect such threats through steganalysis. Crucially, SADBench comprises $4$ core tasks, namely steganography attack capability evaluation, steganalysis defense capability evaluation, efficiency evaluation, and transferability evaluation. It evaluates both image-payload and text-payload steganography across diverse cover distributions, utilizing harmful visual semantics and toxic instructions to simulate malicious attacks. Across a broad set of attacks and detectors, SADBench reveals that (i) INN and autoencoder-based methods demonstrate superior stability compared to other architectures, (ii) in-domain detection is near-perfect and cheaper than generation, (iii) a critical asymmetry exists in transferability where attacks robustly generalize to new distributions while detectors fail to adapt, and (iv) real-world threats persist on social media, where payloads either survive minimal compression or effectively adapt to aggressive compression via simulated training. Overall, SADBench establishes a systematic, reproducible, and extensible framework to quantify risks, paving the way for measurable and security-driven advancements in steganography defense.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
