2. MULTIMEDIA: CRYPTO IS NOT ENOUGH 9/09/2015 | pag. 2

3. Crypto Problem for Multimedia Once decrypted, the multimedia data becomes vulnerable for distribution. Cryptography 9/09/2015 | pag. 3 Watermarking, which is imperceptible message embedding within the work, complements encryption.

4. Watermarking Cryptography 9/09/2015 | pag. 4 Channel Detect Watermark Embed Watermark

5. Modern Applications Copyright Protection Fingerprinting or Traitor Tracing Content Authentication Media Forensics Steganography Database Annotation Device Control … Cryptography 9/09/2015 | pag. 5

6. Basic Technical Issues (1) Transparency (fidelity): embedding information should not cause perceptual degradation of host signal Payload (bit-rate): number of bits that can be embedded in signal Robustness: refers to ability of embedding algorithms to survive common signal processing operations (compression, filtering, noise, cropping, insertions,…) Cryptography 9/09/2015 | pag. 6

7. Basic Technical Issues (2) Security: refers to ability of an adversary to crack information hiding code and design devastating attack to wipe out hidden information (e.g. recovery of host signal which contains no trace of message). Detectability: is the fact of information embedding a secret (cfr. Steganography)?  There exist fundamental trade-offs between transparency, payload or bit rate, robustness, security and detectability. Cryptography 9/09/2015 | pag. 7

8. System issues Does decoder have full, partial, or no knowledge of host signal? Availability of side information generally improves detection BUT introduces communication and storage burden. What security level is needed? What are the attacker’s computational resources? Does attacker have repeated access to decoder? Cryptography 9/09/2015 | pag. 8

9. Standards and Benchmarking So far: NO foolproof watermarking due to lag between theory and practice. Examples: Music industry used watermarking to protect digital music Watermarking as part of international MPEG-4 video standard Recent research groups have developed benchmark tools: Stirmark European Certimark WET project at Purdue university … Cryptography 9/09/2015 | pag. 9

10. Mathematical Models Encoders and decoders: Encoding function x=f(s,m,k) Decoder function m’=g(y,k) *source:Data-Hiding codes (Moulin) Cryptography 9/09/2015 | pag. 10

11. Mathematical Models Attacks: *source:Data-Hiding codes (Moulin) Cryptography 9/09/2015 | pag. 11

12. Mathematical Models Distortion (to characterize perceptual closeness): Squared Euclidian metric (audio, grayscale images (PSNR),…): Hamming distance (binary images):  Fail to capture complexities of human perception  Perceptual models are needed: Popular example is Watson’s metric SSIM Cryptography 9/09/2015 | pag. 12

13. Early Work 1990’s: First papers on data hiding appeared Least significant bit (LSB) techniques Problem: not robust against noise 1995-1998: Spread-spectrum modulation (SSM) codes More robust 1998-…: Quantization Index Modulation Very good performance Cryptography 9/09/2015 | pag. 13

14. LSB codes Host signal s = {s 1, s 2, …,s N } Each s i uses b bits (integer value between 0 and 2 b -1) e.g.: s i = 65 = (01000001) LSB plane is length-N binary sequence made of all LSB’s Information rate is 1 bit per sample. Payload can be increased by replacing second LSB  increase of embedding distortion!  Very weak against noise Cryptography 9/09/2015 | pag. 14

15. LSB codes 9-9-2015 Channel Detection Embedding

16. 1 bitplane 9-9-2015

17. 2 bitplanes 9-9-2015

18. 3 bitplanes 9-9-2015

19. 4 bitplanes 9-9-2015

20. 5 bitplanes 9-9-2015

21. 6 bitplanes 9-9-2015

22. 7 bitplanes 9-9-2015

23. 8 bitplanes 9-9-2015

24. LSB Cryptography 9/09/2015 | pag. 24

25. LSB Cryptography 9/09/2015 | pag. 25

26. Spread-Spectrum Codes Watermarking problem ≈ communication problem with a jammer:  Apply techniques from communications domain  Spread-Spectrum Modulation! Jamming problem: Classic radio/TV transmitter sends signal in relatively narrow frequency band Inappropriate with jammer that allocates all power to that particular band of frequencies Cryptography 9/09/2015 | pag. 26

27. Spread-Spectrum Codes SSM-system: Allocates secret sequences (with broad frequency spectrum) to transmitter. Transmitter sends data by modulating these sequences. Receiver demodulates data using filter matched to secret sequences.  Jammer must spread power over broad frequency range but only small fraction of it will have an effect on communication performance. Cryptography 9/09/2015 | pag. 27

28. Spread-Spectrum Codes Sender: x n = s n + αp n (m,k), 1 ≤ n ≤ N Receiver: y = x + w, w noise Knows secret key k Matching of y to all possible waveforms p(m,k) *source:Data-Hiding codes (Moulin) Cryptography 9/09/2015 | pag. 28

29. Spread-Spectrum Codes Detection: Correlation: Informed detector: *source:Data-Hiding codes (Moulin) Cryptography 9/09/2015 | pag. 29

30. Binning Schemes An important information-theoretic technique, which is widely used. Especially for blind data hiding, which is related/complementary to the problem of transmission with side information. Cryptography 9/09/2015 | pag. 30

31. Binning Schemes Quantization on source sequence S of length N Quantization codebook C of length-N vectors U j Distortion function D VQ problem = find vector U j within codebook C that minimizes distortion D(S, U j ) between observed S and reconstruction vector U j Next, consider M different codebooks C m (= bins) consisting of length-N vector U m,j within C  now we can choose which codebook we want to use!  With M codebooks we can embed k (=log 2 M) bits Cryptography 9/09/2015 | pag. 31

32. Example: Binning Schemes S = binary sequence of length N = 3 (8 possible sequences). We want to embed a information into S, producing a new sequence X. We must satisfy distortion constraint that X and S differ in 1 bit. Transmission of X to a receiver which must decode embedded info without knowledge of original host data S.  Question 1: how many bits can we embed in S?  Question 2: How can we design appropriate scheme? Cryptography 9/09/2015 | pag. 32

33. Binning Schemes example Under distortion constraint, S can be modified in 4 ways: S=010 => X = {000, 110, 010, 011}  2 bits of info can be embedded Consider partition of eight possible X into 4 bins. e.g.: S = 010 and Hamming distance of one Cryptography 9/09/2015 | pag. 33

34. Binning Schemes example Embedding 1-bit message into 7-bit length sequences S. 2 bins allow us to modify 3 bits of S. In this way, it resists the noise with Hamming weight at most 1. Cryptography 9/09/2015 | pag. 34

35. QIM Quantization-Based Codes have been introduced in 1999, by Chen and Wornell, known as Dither Modulation or Quantization Index Modulation (QIM) codes. QIM is a binning scheme. Theses methods embed signal-dependent watermarks using quantization techniques. Cryptography 9/09/2015 | pag. 35

36. Scalar QIM Embedding one bit in a real-valued sample: Here we have 1-bit message m ∈ {0,1}. A scalar, uniform quantizer Q(s) with step size △ is defined as Q(s) = △ [s/ △ ]. We use the function Q(s) to generate two new dithered quantizers. Cryptography 9/09/2015 | pag. 36

37. Scalar QIM The two new dithered Quantizers Q 0 and Q 1 are shown as sets of circles and crosses on the real line. Cryptography 9/09/2015 | pag. 37

38. Original QIM The noisy signal Y = X (Marked signal)+ W. The QIM decoder is a minimum-distance decoder. It finds the quantizer point closest to Y. The quantization errors are uniformly distributed over [- △ /2, △ /2]. This scheme works perfectly, if |W| < △ /4. Cryptography 9/09/2015 | pag. 38

39. Distortion-Compensated Scalar QIM A binning scheme with some error protection against noise (exceeds △ /4). The embedding function is defined as: Cryptography 9/09/2015 | pag. 39

40. Distortion-Compensated Scalar QIM Cryptography 9/09/2015 | pag. 40

41. Other QIM Schemes Sparse QIM Lattice QIM –Another extension of Scalar QIM to the vector case. –Replace the scalar quantizer with a L-dimensional VQ quantizer Cryptography 9/09/2015 | pag. 41

42. Data Hiding in Images Quantization-based codes are widely used to embed data in images. Cryptography 9/09/2015 | pag. 42

43. Data Hiding in Images Lena after data embedding and after attack Cryptography 9/09/2015 | pag. 43

44. Data Hiding in Images 6301 bits was embedded in Lena and tampered with the marked image in various ways Cropping Resizing Substitutions Compression Moderate noise levels All 6301 bits could be successfully decoded. Cryptography 9/09/2015 | pag. 44

45. Conclusion The information-theoretic concept of binning known as the best methods, when the host signal is unavailable to the receiver (blind data hiding). Practical binning schemes show good performance under noise attacks. Spread-spectrum techniques are popular, but have limitations for blind data hiding. Cryptography 9/09/2015 | pag. 45

46. Thank you! … the vast majority of security failures occur at the level of implementation … Cryptography 9/09/2015 | pag. 46