Which attack is described as monitoring resemblance of original data by feeding a detector with data from multiple perspectives?

• A. Distinguishing Statistical attack
• B. Blind Classifier attack
• C. Stego-only Attack
• D. Known-stego Attack

Answer: (B)

Focus on detecting hidden data without relying on knowledge of how it was embedded. A blind classifier attack uses a detector that operates without knowing the embedding method or payload and relies on generalized features to tell whether data looks like natural cover or has been altered. Feeding the detector with data from multiple perspectives helps it compare how the content resembles the original across various views, improving its ability to spot artifacts that come from embedding rather than from the data itself. This combination—a detector that is blind to the embedding method and is exposed to multiple representations—best fits the scenario described.

Distinguishing statistical attacks rely on specific statistical cues to separate cover from stego, but they don’t necessarily involve a detector evaluated across multiple perspectives in a blind way. Stego-only attacks assume you only have stego data with no original for comparison, and known-stego attacks rely on known pairs of cover and stego, which isn’t the situation described.