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Uncover the secrets of concealing and detecting messages using LSB steganography and steganalysis in the digital image domain. Learn about modern techniques and tools for secure communication and data protection.
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Digital Image Steganalysis Kwang-Soo Lee
Outline • Steganography • LSB Steganography • LSB Steganalysis
Cryptography • Cryptography scrambles a message to obscure its meaning. • Today secure communication is often identified with cryptography. • However, cryptographyreveals the fact that communication is happening. ??? @2*$#d(*%7*
Steganography • The word “steganography” comes from Greek, steganos and graphein. • Steganography is the art of hiding information in ordinary-looking objects. • Steganography aims to conceal the existence of secret communication.
Classical Steganography • Examples: • Hidden tattoo, • Covered writing, • Invisible ink, • Microdots, • Character arrangement, • Paper mask, • etc. • Hiding a secret message in physical objects. • Secrecy depends on keeping the methods secret.
Modern Steganography • Hiding information in digital objects, Invisibly. • The Invisibility must depend on just the stego-key, not the stego system.
LSB Steganography • Replacing least-significant-bits (LSBs) of digital data with message bits. • Using digital multimedia, such as image, audio, video, as cover-objects. • Embedding random message bits in LSBs will not cause any discernable difference from the cover-signals. • Easy to implement, High payloads. Embedding 11001000 Extracting
Digital Images for Steganography • Types of digital images: • binary, gray-scale, RGB color, palette, JPEG, etc. • The LSB plane of image data looks like random noise. • Bit-plane decomposition of the Lena image in gray-scale. lena.bmp 6th Bit Plane 4th Bit Plane LSB Plane
LSB Steganalysis • Steganalysis is the science of detecting hidden messages in digital signals. • It takes advantage of statistical or perceptual distinction of stego-signals from cover-signals. • LSB steganalysis • Visual attack, • histogram analysis (PoV analysis), • Closed color analysis, • Regular-singular (RS) analysis, • Sample pair (SP) analysis, • LR Cube analysis, • Etc.
PoV analysis • Proposed by Westfeld and Pfizmann (IH 1999) . • PoV means a pair of values which differ just in the LSBs. • 0 1 2 3 4 5 6 7 8 9 10 …… • LSB embedding tends to equalize those frequencies of the values of each PoV. LSB Embedding stego-image histogram cover-image histogram
Sample Pair Analysis • Proposed by Dumitrescu et al. (IH 2003) • Based on symmetry of quantized noise distribution. • Take advantage of spatial correlation such as pixel adjacency. • Estimate the length of hidden message. • Outperform PoV analysis. LSB Embedding stego-image histogram cover-image histogram
LR Cube Analysis • Left and Right cube analysis (LRCA), developed by us (IH 2005) • Our method uses high dim. vectors as basic units drawn from digital signals. • Consider the vector noise distribution and its distortion of LSB embedding.
LR Cube Analysis • Left cube and Right cube, and the possible cube patterns or complexities. • Cover-signals show similar complex levels between the left cubes and the right cubes, but these are not the case for stego-signals after the LSB embedding • LRCA works by measuring the similarities between these two distributions.
