GenStegoBench: Auditing Persistent Covert Channels in Latent Generative Image Systems

Modern generative image systems are increasingly built on diffusion or flow-style iterative generation, where images can often be approximately inverted to latent or noise representations. This generation–recovery property creates a security concern: an ordinary-looking generated image may carry a covert message that remains decodable after common online processing. We introduce GenStegoBench, a security audit benchmark for measuring when generative-image steganographic channels persist, when they fail, and which transformations can disrupt them. Our audit provides both attacker- and defender-side insights: generator backbones and solvers transport decoder margins and can strengthen hidden-channel reliability, while common image transformations break different recoverable invariants. GenStegoBench audits covert-channel persistence through the generation–channel–recovery lifecycle of generative images.