MULTIMEDIA:
CRYPTO IS NOT ENOUGH
8/04/2015 | pag. 2
Introduct ion
Crypto Problem for
Multimedia
Cryptology
Wat ermarking
Watermarking
Once decrypted, the multimedia data becomes
vulnerable for distribution.
Once unscrambled (decrypted), the video data becomes vulnerable
for distribution.
Watermarking,
is imperceptible
message
Watermarking,which
which is imperceptible
message embedding,
complements encryption.
embedding
within the work, complements encryption.
Cryptography
8/04/2015 | pag. 3
Watermarking
Embed
Watermark
Channel
Detect
Watermark
Cryptography
8/04/2015 | pag. 4
Modern Applications
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•
•
•
•
•
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Copyright Protection
Fingerprinting or Traitor Tracing
Content Authentication
Media Forensics
Steganography
Database Annotation
Device Control
…
Cryptography
8/04/2015 | pag. 5
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
8/04/2015 | pag. 6
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
8/04/2015 | pag. 7
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
8/04/2015 | pag. 8
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:
•
•
•
•
Cryptography
8/04/2015 | pag. 9
Stirmark
European Certimark
WET project at Purdue university
…
Mathematical Models
Encoders and decoders:
• Encoding function
x=f(s,m,k)
• Decoder function
m’=g(y,k)
*source:Data-Hiding codes (Moulin)
Cryptography
8/04/2015 | pag. 10
Mathematical Models
Attacks:
*source:Data-Hiding codes (Moulin)
Cryptography
8/04/2015 | pag. 11
Mathematical Models
• Distortion (to characterize perceptual closeness):
Squared Euclidian metric (audio, grayscale images (PSNR),…):
d E (s,x) =|| s - x ||2 , if S = X =
Hamming distance (binary images):
n
d H (s,x) | n : sn  xn  | , if S  X  {0,1}
 Fail to capture complexities of human perception
 Perceptual models are needed:
• Popular example is Watson’s metric
• SSIM
Cryptography
8/04/2015 | pag. 12
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
8/04/2015 | pag. 13
LSB codes
• Host signal s = {s1, s2, … ,sN}
• Each si uses b bits (integer value between 0 and 2b-1)
e.g.: si = 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
8/04/2015 | pag. 14
LSB codes
Channel
Embedding
Detection
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1 bitplane
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2 bitplanes
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3 bitplanes
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4 bitplanes
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5 bitplanes
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6 bitplanes
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7 bitplanes
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8 bitplanes
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LSB
Cryptography
8/04/2015 | pag. 24
LSB
Cryptography
8/04/2015 | pag. 25
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
8/04/2015 | pag. 26
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
8/04/2015 | pag. 27
Spread-Spectrum Codes
• Sender:
xn = sn + αpn(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
8/04/2015 | pag. 28
Spread-Spectrum Codes
Detection:
• Correlation:
• Informed detector:
*source:Data-Hiding codes (Moulin)
Cryptography
8/04/2015 | pag. 29
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
8/04/2015 | pag. 30
Binning Schemes
• Quantization on source sequence S of length N
• Quantization codebook C of length-N vectors Uj
• Distortion function D
• VQ problem = find vector Uj within codebook C that
minimizes distortion D(S, Uj) between observed S and
reconstruction vector Uj
• Next, consider M different codebooks Cm (= bins)
consisting of length-N vector Um,j within C
 now we can choose which codebook we want to use!
 With M codebooks we can embed k (=log2M) bits
Cryptography
8/04/2015 | pag. 31
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
8/04/2015 | pag. 32
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
8/04/2015 | pag. 33
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
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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
8/04/2015 | pag. 35
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
8/04/2015 | pag. 36
Scalar QIM
The two new dithered Quantizers Q0 and Q1 are shown
as sets of circles and crosses on the real line.
Cryptography
8/04/2015 | pag. 37
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
8/04/2015 | pag. 38
Distortion-Compensated
Scalar QIM
• A binning scheme with some error protection against
noise (exceeds △/4).
• The embedding function is defined as:
Cryptography
8/04/2015 | pag. 39
Distortion-Compensated
Scalar QIM
Cryptography
8/04/2015 | pag. 40
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
8/04/2015 | pag. 41
Data Hiding in Images
Quantization-based codes are widely used to embed data
in images.
Cryptography
8/04/2015 | pag. 42
Data Hiding in Images
Lena after data embedding and after attack
Cryptography
8/04/2015 | pag. 43
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
8/04/2015 | pag. 44
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
8/04/2015 | pag. 45
Thank you!
… the vast majority of security failures
occur at the level of implementation …
Cryptography
8/04/2015 | pag. 46