Image Steganography Using Fuzzy Domain Transformation and Pixel Classification

1. Image Steganography Using Fuzzy Domain Transformation and Pixel Classification Aleem Khalid Alvi Robin Dawes School of Computing Queen’s University, Kingston, Canada

2. Contents • Information Hiding Methods • Proposed Technique • Fuzzy Image Representation and Domain Transformation • Gaussian Membership Function (GMF) • Fuzzy Inference System (FIS) • Methodology • Using Image Processing for Fuzzy Pixel Classification • Analysis and Results • Conclusion

3. Information Hiding Methods • Attributes for classification of information hiding methods • Cover objects • Secret objects • Hiding techniques • Current technologies • Steganography system characteristics • Robustness • Security • Un-detectability • Imperceptibility (invisibility or perceptual transparency) • High capacity

4. Proposed Technique • Proposed technique is the combination of • Domain transformation • Data conversion • Substitution • Image properties • It is kind of private-key steganography technique

5. Fuzzy Image Representation and Domain Transformation • An image representation in spatial and fuzzy

6. Gaussian Membership Function (GMF) We use GMF for image transformation from the spatial domain into the fuzzy domain • The specific image transformation function with fuzzifier Where fh= fuzzifier, Imax = maximum pixel value of an image, Imn= any gray level pixel value of an image I

7. Fuzzy Inference System (FIS) • We use Mamdani fuzzy interference system (FIS) • Using the fuzzy inference process, • A given input (a crisp input) maps to an output (a crisp output) using fuzzy logic methods. • The fuzzy inference process requires membership functions, logical operations, and If-Then rules. • Implementation steps • Fuzzify inputs • Apply fuzzy operator • Apply implication method • Aggregate all outputs • Defuzzify

8. Methodology The step-by-step methodological information for embedding process on the sending end of the steganography System.

9. Using Image Processing for Fuzzy Pixel Classification • Use fuzzy based If-Then rules to apply fuzzy classification • Select the appropriate cover pixel for embedding secret data • Produce less disturbance and distortion in the embedded cover image with respect to Human Visual System (HVS) • Use texture and silhouette (edge) properties of an image

10. Analysis and Results • Using Lena (Cover) and Tomahawk Missile (Secret) Images • Lena.jpg available capacity = 145,313 pixels • Secret data uses 17.62% of the available capacity • HVS testing shows that original and stego images have significant difference and visible as light shades • Statistical testing shows differences • Cover histograms looks similar

11. Analysis and Results contd.. • Using Baboon (Cover) and Tomahawk Missile (Secret) Images • Baboon.jpg has available capacity = 138,518 pixels • Uses 18.48% available capacity • HVS testing shows that no difference in visibility • Statistical testing shows the difference between their statistical values • Cover histograms looks similar

12. Conclusion • Proposed steganography algorithm based on fuzzy inference system • Fuzzy inference system uses fuzzy transformation and pixel classification techniques • The fuzzy pixel classification uses the image processing techniques by exploiting texture and silhouette properties • The exploitation of the image processing techniques with fuzzy logic increase imperceptibility in stego image significantly

13. Thank You!