Which attack uses a chi-square statistical test to determine embedding by examining frequency changes?

Which attack uses a chi-square statistical test to determine embedding by examining frequency changes?

• A. Distinguishing Statistical attack
• B. Chi-square Attack
• C. Blind Classifier attack
• D. zsteg

Answer: (B)

This tests detecting steganography by using a chi-square statistic to see if the frequency distribution of pixel values has been altered by embedding. When data is hidden in image pixels, especially with LSB techniques, the act of flipping bits changes how often certain pixel values occur. The chi-square attack computes how the observed histogram of pixel values (or related pair frequencies) fits with what would be expected if no data were hidden. If the observed distribution deviates significantly, it signals that embedding likely took place. That’s why this is the best choice: it relies on a formal chi-square goodness-of-fit test to quantify the change in frequency patterns caused by embedding. Other options describe broader or different approaches (general statistical distinctions, machine-learning classifiers, or a tool for visual inspection) that don’t specifically embody the chi-square frequency-change method.

This tests detecting steganography by using a chi-square statistic to see if the frequency distribution of pixel values has been altered by embedding. When data is hidden in image pixels, especially with LSB techniques, the act of flipping bits changes how often certain pixel values occur. The chi-square attack computes how the observed histogram of pixel values (or related pair frequencies) fits with what would be expected if no data were hidden. If the observed distribution deviates significantly, it signals that embedding likely took place.

That’s why this is the best choice: it relies on a formal chi-square goodness-of-fit test to quantify the change in frequency patterns caused by embedding. Other options describe broader or different approaches (general statistical distinctions, machine-learning classifiers, or a tool for visual inspection) that don’t specifically embody the chi-square frequency-change method.