Information and Communications Security: Encryption and Information Hiding

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Cordelia Holt
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1 Short Course on Information and Communications Security: Encryption and Information Hiding Tuesday, 10 March Friday, 13 March, 2015 Lecture 10: Information Hiding

2 Contents Covert Encryption Principles of Steganography Overview of Watermarking Methods Information Hiding: A Signal Processing Model Hiding information in Noise: Fractal Modulation Summary

3 Covert Encryption Information Hiding Covert Encryption Related issues include: Camouflage Disinformation Authentication Self-authentication Steganography Watermarking

4 Steganography Steganography (Greek in origin) means Covered or Concealed Writing

5 Watermarking and Authentication

6 Camouflage and Disinformation

7 Why Should Encrypted Information be Transmitted Covertly?

8 Stegacryptography using Noise Fractal Modulation

9 Principals of Steganography

10 Problem Definition

11 Existing Solutions Physical techniques Digital techniques Micro printing Digital watermarking Foil hologram (overt) Printing using ultraviolet sensitive inks (covert)

12 Watermarking.v. Steganography

13 Watermarking.v. Cryptography

14 A Framework for Digital Watermarking

15 Applications of Digital Watermarking Owner Identification Embedding the identity of the owner to prevent other parties from claiming the copyright of the data Labelling A hidden message can contain labels that allow for annotation of documents Fingerprinting (transaction tracking) Identify people with legal status but illegally redistribute it Authentication Embedding signature information in data that can be later checked to verify whether or not if has been tampered with

16 Watermarking Classifications According to inputs and outputs Spatial domain techniques Frequency domain techniques According to workspace used Private Semi-private Public According to visibility Hidden Visible Noticeable to trained users

17 Watermarking Techniques Spatial techniques Frequency techniques Encoded by directly modifying pixels E.g. flipping low-order bits of selected pixels Simple to implement Require a lower computational cost They can be less robust Encoded by altering some frequency coefficients obtained by transforming the image into the frequency domain, such as DFT DWT DCT

18 Classifications (Continued) According to robustness Robust Fragile According to watermark nature Pseudo-random sequence Small logo images/signals

19 Watermarking Properties Fidelity Perceptual similarity between the original and the watermarked version must be very high Statistical invisible Data payload Embedding effectiveness A statistical analysis should not produce any advantage from the attacking point of view Refers to the amount of information that can be carried in a watermarked cover The probability that the embedder will successfully embed a watermark in a randomly selected data field

20 Properties (continued) False positive rate The frequency with which we should expect a watermark to be detected in a non-watermarked object (which must be low). Robustness The ability of the watermark to resist hostile attacks and to survive normal processing of the content such as spatial filtering and lossy compression. Readily extracted If the decoder needs to run in real-time, then it is necessary for the decoding process to be significantly simpler than the encoding process.

21 Attacks classifications Robustness attacks Unauthorized removal: Aim to diminish or remove the presence of a digital watermark. Presentation attacks Interpretation attacks Masking attacks: Alters the content so that the watermark can no longer be detected or extracted easily. Seeks to forge invalid or multiple interpretations from watermark evidence. Legal attacks The attacker uses a legal precedent to establish doubt in court.

22 Information Hiding: A Signal Processing Model Diffusion + Confusion Hiding condition

23 Information Retrieval 1: Diffuser/Covertext Retrieval Requires knowledge of both processor and covertext Inverse operator must be computationally stable If the covertext is a cipher, then retrieval is dependent on knowledge of a private key

24 Information Retrieval 2: Diffuser Only Retrieval Requires knowledge of processor only Any covertext can be used provided Require a diffuser such that: - the inverse operator is computationally stable - simple to implement

25 Hiding Data in Images Stegotext image = Covertext image + Plaintext image

26 Hiding information in noise Noise is a good medium for hiding data (encrypted or otherwise) especially when noisy transmission environment are available Three approaches can be used: - embedding the ciphertext in real noise; - transforming the ciphertext into noise that is added to data; - replacing real noise with ciphertext that has been transformed in to synthetic noise with exactly the same properties as the real noise: Fractal Modulation

27 Example of Fractal Noise Poisson model Real Internet traffic Random fractal model The shaded areas highlight the data displayed in each plot above respectively.

28 Fractal Geometry: Underlying Philosophy In every way one can see the shape of the sea copyright i

29 Fractals and Texture copyright i Much of Fractal Geometry can be considered to be an intrinsic study of texture B Mandelbrot

30 Fractal Types

31 Fractal Clouds: D=2.1

32 Fractal Clouds: D=2.2

33 Fractal Clouds: D=2.3

34 Fractal Clouds: D=2.4

35 Fractal Clouds: D=2.5

36 Fractal Clouds: D=2.6

37 Fractal Clouds: D=2.7

38 Fractal Clouds: D=2.8

39 Fractal Clouds: D=2.9

40 Fractal Modulation: theoretical basis Non-stationary fractal signal given by where n is white noise. Let q(t) be assigned two values which correspond to 0 and 1 in a bit stream respectively over a fixed period of time

41 Fractal modulation/demodulation Fractal modulation: Given q(t) compute u(t) Fractal demodulation: Given u(t) compute q(t) in the presence of additive noise For Computation details of algorithm see Information Hiding, Watermarking and Steganography: Methods and Applications, J M Blackledge Lecture Notes, Series 6, 2010

42 Example of Fractal Modulation

43 Summary

Filtering. -If we denote the original image as f(x,y), then the noisy image can be denoted as f(x,y)+n(x,y) where n(x,y) is a cosine function.

Filtering. -If we denote the original image as f(x,y), then the noisy image can be denoted as f(x,y)+n(x,y) where n(x,y) is a cosine function. Filtering -The image shown below has been generated by adding some noise in the form of a cosine function. -If we denote the original image as f(x,y), then the noisy image can be denoted as f(x,y)+n(x,y)