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Steganography and Biometrics
For
Remote Authentication
Sabah A. Jassim
Applied Computing Department
Summer University – Un...
Outline
• Biometric Authentication – Introduction
• Remote Biometric Authentication – The challenge
• The Steganography So...
Biometrics - Introduction
 Biometrics are digital representations of physiological, behavioral
or combined characteristic...
EU Funded FP6 Project (2004-2006)
 Aims to produce a prototype of a new mobile communication system enabling
biometricall...
Solutions: Fusion of 2 or more Biometric modalities
 PDA’s have multi-sensors, that can be used to Fuse two or more
accep...
SecurePhone – The Biometric verifier
preprocessing
modellingmodellingmodelling
preprocessingpreprocessing
face voice signa...
Match on Host (MoH)
Modality 5 digits 10 digits
Voice (V) 6.1 3.4
Face (F) 28.6 29.9
Signature (S) 6.2 6.2
V + F 4.8 3.0
V...
Biometrics Authentication – Expectations and Myths
 Biometric-based authentication are widely perceived to be more
reliab...
Template Security Approaches
 Encryption of templates does not provide security.
 Biometric cryptosystem - Use the biome...
Digital Steganography - Introduction
Sender Receiver
Cover Cover
Stego
Warden
Digital Steganography – Introduction
 The process of hiding a secret message imperceptibly in
an innocent looking image w...
Secret Hiding in Images – Main Approaches
 Hiding in the cover image spatial domain
– The Bit planes
• Embedding in the L...
LSB Technique
Replace the LSB of the first 8 pixels of the cover image with that of the 8
bits of the first character B:
1...
Steganogrpahy solution – The BUT requirements
 Biometric trait samples must be easy to transform
and represent by binary ...
Face Biometrics
 Your Face is your public identity.
 Natural Non-invasive mode of person identification
 More acceptabl...
Factors impacting Face Recognition deployment
 Curse of Dimension: A face image defines a high dimensional
vector and a h...
Face Recognition – Main Approaches
 Geometric Feature matching (not sufficient, but has some use).
 Appearance-based mat...
PCA based Face Recognition
Face images are not scattered in their high dimensional space, but
rather cluster/reside in mu...
Trained 200 face images from the ORL database
10 most significant eigenfaces out of the 200 eigenfaces
Example
Good recognition: How many Eigenfaces needed?
DWT – Face Recognition Schemes
The Discrete Wavelet Transform (DWT) decomposes
an image into a set of different frequency...
Verification stage
compareusinga
similaritymeasure
new
face image
projection onto
Eigenspace
k training
face images
multi-...
Deep Learning - ConvNets
• Convolutional Neural Network – originally developed in 1980’s.
• Successive filtering and downs...
Suitability for Steganogrpahy solution!!
 Most schemes assume that capturing fresh biometric
samples are completely contr...
Local Binary Patterns (Ojala et al., 1994, 2002)
 The Local Binary Pattern (LBP) is an image operator that
encapsulates t...
LBP - Example
Example Threshold Weights Result
7 0 8 1 0 1 1 2 4
2 4 9 0 1 128 8 93
4 3 5 1 0 1 64 32 16
Window of 3×3 Pat...
LBP Histograms
 An LBP code is uniform if it contains no more than two
circular transitions from 0 to 1 or from 1 to 0.
e...
LBP – based Face recognition (Ahonent et al., 2014)
 The LBP histogram (LBPH) of whole face image encodes only the
occurr...
LBP – based Face recognition in wavelet domain
 The 3x3 based LBP operator cannot capture dominant large
scale texture fe...
LL HL LH HH
Original
LBP using N8
LBP using N4
Example – Wavelet based LBPs 8-bits and 4-bits
Face Feature Extraction (LBP of Wavelet sub-bands)
• Select sub-band(s), determine the 8 or 4 LBPHs for each
blocks of the...
LSB types of Embedding
 Most existing LSB based embedding are defeated by a
targeted statistically-based steganalysis too...
LSB-Witness: Embedding type 1.
• Instead of changing the 1st LSB plane of the cover, we
change the 2nd LSB plane.
• The me...
LSB-Witness: Extracting
• Extractor checks the second LSB plane which acts as a
witness to the embedded bits
• Extracted b...
Experimental Data and Evaluation Protocol
• Cover images: five images of size 512×512
– Yale Face Database
• ORL database
...
DWT LBP- Face Recognition - Accuracy
• Recognition accuracy (%), Yale database, 50% training 50% testing, with 5x5 blocks
...
Payload vs. imperceptibility
• Number of bins used in each case
• PSNR in different combination of sub-bands, each sub-ban...
Robustness against LSB targeted steganalysis
 None of the two most common steganalysis
techniques PoV and RS can detect t...
Hiding LBPHs in 8-bit pattern matching (2)
0 1 0 1 0 0 1 0 0 1 0 1 1 0 0 1
0 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1
0 1 0 0 1 1 1 1...
Block selection strategies
 The number of blocks in a wavelet sub-band is much
smaller than the number of blocks in the c...
Results (Invisibility in terms of PSNR)
Payload Traditional LSB 8-bits Pattern 4-bits Pattern
22% 57.535 62.845 63.624
45%...
Results (Detectability)
Weighted Steganalysis
Payload Technique used Baboon
Camera
Man
Lena
Living
Room
Blonde
Women
0 -0....
Robustness against LSB targeted steganalysis
 The two most relevant targeted steganalysis tools of WS
and DIH cannot dete...
Analysis
 The embedded feature do not leak the original data
 Experiments show that 83-85% of N8 patterns in secret
biom...
Remote Face based Authentication – An outline
 A number of different Remote authentication protocols can
be designed to u...
Conclusions (Biometrics embedding)
• LBP was applied on wavelet sub-bands and features were
extracted from multiple wavele...
Applications
Sender
Receiver
Cover
Cover
Acknowledgement
Thank you for your attention.
Questions?
sabah.jassim@buckingham.ac.uk
The End
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Esteganografía y criptosistemas biométricos para la autenticación mutua en el intercambio de claves // Steganography and biometric cryptosystems for mutual authentication in the exchange of passwords

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Esteganografía y criptosistemas biométricos para la autenticación mutua en el intercambio de claves
Por Sabah Jassim.
Universidad de Buckingham.
Curso de Verano 'Innovación en ciberseguridad aplicada a la protección de la identidad digital #CIGTR2015'.
http://es.cigtr.info/2015/06/curso-de-verano-innovacion-en-seguridad.html (ES)

Steganography and biometric cryptosystems for mutual authentication in the exchange of passwords
By Sabah Jassim.
University of Buckingham.
Summer Course 'Innovation in security applied to the protection of digital identity #CIGTR2015'.
http://www.cigtr.info/2015/06/summer-course-innovation-in-security.html#.ViXv337hDb2 (EN)

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Esteganografía y criptosistemas biométricos para la autenticación mutua en el intercambio de claves // Steganography and biometric cryptosystems for mutual authentication in the exchange of passwords

  1. 1. Steganography and Biometrics For Remote Authentication Sabah A. Jassim Applied Computing Department Summer University – Universidad Rey Juan Carlos- Spain 6th -8th July 2015
  2. 2. Outline • Biometric Authentication – Introduction • Remote Biometric Authentication – The challenge • The Steganography Solution - Requirements • Digital Steganography – Approaches and Challenges • The SecurePhone project & Recent Face Recognition • Suitable face biometric for Remote Authentication • Steganography for remote Biometric authentication
  3. 3. Biometrics - Introduction  Biometrics are digital representations of physiological, behavioral or combined characteristics of human  Provides proof of identity for access to sensitive locations, entitlement to services, and for fighting crime and terrorism.  Widely accepted as the most effective mechanism for authentication for access control and e-banking. Privacy concerns is an obstacle!!
  4. 4. EU Funded FP6 Project (2004-2006)  Aims to produce a prototype of a new mobile communication system enabling biometrically authenticated users to deal legal m-contracts during a mobile phone call in an easy yet highly dependable and secure way using a biometric recogniser that fuses face, voice and handwritten signature. The SP consortuim SecurePhone
  5. 5. Solutions: Fusion of 2 or more Biometric modalities  PDA’s have multi-sensors, that can be used to Fuse two or more acceptable biometrics, perhaps none is reliable, but together produce more reliable identification. SSL - Secure PKI (Public Key Infrastructure)
  6. 6. SecurePhone – The Biometric verifier preprocessing modellingmodellingmodelling preprocessingpreprocessing face voice signature accept & release private key reject user fusion client & impostor joint-score models  Zero-Knowledge Authentication.
  7. 7. Match on Host (MoH) Modality 5 digits 10 digits Voice (V) 6.1 3.4 Face (F) 28.6 29.9 Signature (S) 6.2 6.2 V + F 4.8 3.0 V + S 1.1 0.7 S + F 4.8 4.7 V + F + S 0.9 0.6 Result table with improved results for 5-digit and 10-digit prompts in PDAtabase (SPIE 2006) This has been improved to around 20% and even lower!
  8. 8. Biometrics Authentication – Expectations and Myths  Biometric-based authentication are widely perceived to be more reliable than traditional schemes.  But biometric systems are vulnerable to many attacks: – Templates might be stolen and replaced by imposer’s template – A physical spoof can be created from biometric templates, e.g.  Hill climbing generates target templates (Adler, 2005)  Fingerprint were constructed from standard templates (Cappelli et al, 2007).  Protecting Templates in Remote Authentication is a serious Challenge to deployment of biometrics!!!
  9. 9. Template Security Approaches  Encryption of templates does not provide security.  Biometric cryptosystem - Use the biometric trait to generate a key, or a biometric hash as a proof of identity.  Feature transformation – Matching in the secure transformed domain. Invertible or non-invertible transforms are often generated from a secret key. Examples include salting, secure sketches, and random projections.  Steganography – Hiding templates in mundane communication transactions can conceal the act of authentication. BUT reasonable conditions are needed (Our Contribution)
  10. 10. Digital Steganography - Introduction Sender Receiver Cover Cover Stego Warden
  11. 11. Digital Steganography – Introduction  The process of hiding a secret message imperceptibly in an innocent looking image which can be transmitting over an open channel without being detected  Performance indicators include: – Capacity: How much data can be hidden in an image – Cover Quality: Invisibility of Artefacts – Robustness: Ability to avoid detection by steganalysis – Security : secret cannot be extracted even if detected  Designing a high performing steganography system in terms of all these indicators is a tough challenge.
  12. 12. Secret Hiding in Images – Main Approaches  Hiding in the cover image spatial domain – The Bit planes • Embedding in the Least Significant Bit (LSB) • Embedding in other than LSB, such as 2LSB – Selected Locations • Sequential, • randomly, • In edge regions, etc.  Hiding in the image frequency domain (DCT, Wavelet) Here we are concerned with spatial domain schemes.
  13. 13. LSB Technique Replace the LSB of the first 8 pixels of the cover image with that of the 8 bits of the first character B: 13010 = 100000102 embed  100000102 = 13010 (no change) 13210 = 100001002 embed  100001012 = 13310 (𝑐ℎ𝑎𝑛𝑔𝑒𝑑) Easy but not secure, anyone can extract the secret. Embed a secret/sensitive message ‘Biometrics’ using LSB replacement. Convert the message to binary: B (66) = 0100 0010 i (105) = 0110 1001 Each character requires 8 pixels from the cover image (pixel values: 130, 132, …, 140)
  14. 14. Steganogrpahy solution – The BUT requirements  Biometric trait samples must be easy to transform and represent by binary strings  Capacity and invisibility requirements require the secret to be sufficiently short.  For security and privacy reasons, the process of obtaining the binary secret string cannot be reversed to obtain the original biometric trait sample  The act of embedding the secret should preserve the accuracy of matching.
  15. 15. Face Biometrics  Your Face is your public identity.  Natural Non-invasive mode of person identification  More acceptable biometric than many other, probably more sophisticated and reliable, biometrics.  Most people own programmable mobile smart phones endowed with reasonably good quality web camera.  Research into face biometrics have matured over the last two decades with significantly improved accuracy . A variety of face recognition schemes have been developed and many challenges have been dealt with.
  16. 16. Factors impacting Face Recognition deployment  Curse of Dimension: A face image defines a high dimensional vector and a huge size digital file. Processing and analyses of images become computationally demanding.  Significant variation in appearance due to changes in Expression, Pose, Aging, Lighting conditions, Distance from Camera, Low resolution and Image degradation (CCTV). Effect of these variations maybe controlled in our application  Privacy Concerns. Remote Biometric based authentication.
  17. 17. Face Recognition – Main Approaches  Geometric Feature matching (not sufficient, but has some use).  Appearance-based matching:  Dimension reducing schemes: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), Isometric projections, ...  Statistical Models (GMM and HMM).  Elastic Graph Matching.  Most schemes are based on 2D images, but 3D Head model have also been used – Independent of pose and orientation.
  18. 18. PCA based Face Recognition Face images are not scattered in their high dimensional space, but rather cluster/reside in much lower dimensional subspace. PCA is based on using the most significant eigenvalues of the covariance matrix of a training set  ={T1 ,T2 ,T3 , … ,Tм} of face images: image.averagetheisAand,w here , )( )( 1 1 AT M C ii T i M i i     The corresponding eigenvectors are used to construct face-like images known as Eigenfaces.  Projecting a face image onto the eigenspace reduces its dimension to the number of chosen most significant eigenfaces.
  19. 19. Trained 200 face images from the ORL database 10 most significant eigenfaces out of the 200 eigenfaces Example
  20. 20. Good recognition: How many Eigenfaces needed?
  21. 21. DWT – Face Recognition Schemes The Discrete Wavelet Transform (DWT) decomposes an image into a set of different frequency subbands with different resolutions. At a resolution depth of k, the pyramidal scheme decomposes an image I into 3k + 1 subbands: (LLk, HLk, LHk, HHk, . . . , HL1, LH1, HH1). LLk represents the k-level approximation of the image The subbands HL1, LH1, and HH1 contain finest scale coefficients, and the coefficients get coarser as k increases, LLk being the coarsest.  Each subband represents the face at different frequency ranges and scales.  The coefficients in each subband can be used as a distinct stream for face recognition that can be fused for improved accuracy.
  22. 22. Verification stage compareusinga similaritymeasure new face image projection onto Eigenspace k training face images multi-stage DWT PCA set of feature vectors set of Eigenfaces Enrolment stage multi-stage DWT feature vector Face verification/recognition Research at UB
  23. 23. Deep Learning - ConvNets • Convolutional Neural Network – originally developed in 1980’s. • Successive filtering and downsampling ‘layers’ where network learns optimal ‘weights’ through iterative training procedure. (Inefficient training) • Recent advances and efficient training methods includes: - Microsoft (DeepID) – 200K training images - Facebook (Deepface) - 7.4M training images - Google (DeepNet) – 260M training images!!!
  24. 24. Suitability for Steganogrpahy solution!!  Most schemes assume that capturing fresh biometric samples are completely controlled by the system  Unfortunately these schemes are not suitable but for different reasons.  Most can be binarised but into rather long strings from which the recorded sample can be approximated.  SecurePhone enable user devices to authenticate owners locally to sign deals remotely without sending biometrics.  A newly developed face recognition scheme can be modified to meet the stated requirements
  25. 25. Local Binary Patterns (Ojala et al., 1994, 2002)  The Local Binary Pattern (LBP) is an image operator that encapsulates the local texture and is illumination invariant  It replaces each pixel value g(x,y) with an 8-bit binary code whose bits indicate the order relation between g(x,y) with the grey-values in its 3×3 window neighbourhood processed in a “clockwise” direction starting from the top left corner pixel. For each neighbouring pixel p, if g(x,y) > g(p) then the corresponding bit is set to 0 else it would be set to 1, i.e. 𝐿𝐵𝑃 𝑥 𝑐, 𝑦𝑐 = 𝑛=0 𝑛=7 𝑠 𝑖 𝑛 − 𝑖 𝑐 2 𝑛 , 𝑤ℎ𝑒𝑟𝑒 𝑠 𝑥 = 1 𝑖𝑓 𝑥 ≥ 0 0 𝑖𝑓 𝑥 < 0
  26. 26. LBP - Example Example Threshold Weights Result 7 0 8 1 0 1 1 2 4 2 4 9 0 1 128 8 93 4 3 5 1 0 1 64 32 16 Window of 3×3 Patterns=01011101 LBP=1+4+8+16+64= 93 (a) (b) (c) (d)  Other versions of the LBP includes changing order of pixels, window size, and/or a sub-pattern of neighbouring pixels.  LBP can also be defined in the frequency domain (e.g. DWT)  Histograms of LBP patterns are used for face recognition
  27. 27. LBP Histograms  An LBP code is uniform if it contains no more than two circular transitions from 0 to 1 or from 1 to 0. e.g. 00000000 and 01111110 are uniform whereas 11101101 and 01011011 are non- uniform patterns.  There are only 58 uniform LBP-patterns, making about 90% of LBP patterns in images. Uniform patterns are sufficient to describe image texture.  LBP histogram of 59 bins are sufficient: one for each of the 58 uniform patterns and one for the non-uniform ones.
  28. 28. LBP – based Face recognition (Ahonent et al., 2014)  The LBP histogram (LBPH) of whole face image encodes only the occurrences of the micro-patterns but no spatial support.  To encode local texture and global ‘shape information’ of a face, Ahonen et al. proposed a block based scheme that concatenate their LBPHs for a single feature histogram of the face.
  29. 29. LBP – based Face recognition in wavelet domain  The 3x3 based LBP operator cannot capture dominant large scale texture feature.  LBP in different DWT subbands provides a texture analysis tool in different frequency ranges and scales.  To reduce the payload capacity for hiding face LBPH’s and to maintain stego-image quality, our LBP face recognition in the wavelet uses only 4 neighbours. Below is one of 2 options:  The LBPH of 4-bits codes require only 16 bins (not 59 bins). 12 1 Binary: 1001 20 10 9 1 0 Decimal: 9 7 0
  30. 30. LL HL LH HH Original LBP using N8 LBP using N4 Example – Wavelet based LBPs 8-bits and 4-bits
  31. 31. Face Feature Extraction (LBP of Wavelet sub-bands) • Select sub-band(s), determine the 8 or 4 LBPHs for each blocks of the original face image . • A face image is decomposed using Haar WT LL2 HL2 HL1 LH2 HH2 LH1 HH1 12 1 Binary: 1001 20C 10 9 1 0 Decimal: 9 7 0 LBP histogram has 59 bins for 8- LBP scheme, and 16 bins for the 4- LBP scheme.
  32. 32. LSB types of Embedding  Most existing LSB based embedding are defeated by a targeted statistically-based steganalysis tool.  The RS (Fridrich, et al., 2001), PoV (Westfeld & Pfitzmann, 2000), WS (Fridrich & Goljan, 2004; Ker & Bohme, 2008) and the difference image histogram (DIH) (Zhang & Ping, 2003) are designed to break the LSB replacement scheme.  While Andrew Ker (2005) is used to break the steganography LSB matching embedding techniques (Sharp, 2001)  We have designed different embedding schemes that are not detected by any of the above. .
  33. 33. LSB-Witness: Embedding type 1. • Instead of changing the 1st LSB plane of the cover, we change the 2nd LSB plane. • The message is in the 1st LSB plane Secret bit 1st LSB 2nd LSB (Secret bit XOR 1st LSB) 0 0 0 0 1 1 1 0 1 1 1 0 • The 2nd LSB of the cover acts as a ‘witness’ [1] Rasber D. Rashid, Harin Sellahewa and Sabah A. Jassim, “Biometric feature embedding using robust steganography technique,” in Proceedings of SPIE 8755, Mobile Multimedia/Image Processing, Security, and Applications 2013, 875503 (May , 2013).
  34. 34. LSB-Witness: Extracting • Extractor checks the second LSB plane which acts as a witness to the embedded bits • Extracted bit = 1st LSB XOR 2nd LSB 2nd LSB 1st LSB Extracted bit 0 0 0 0 1 1 1 0 1 1 1 0
  35. 35. Experimental Data and Evaluation Protocol • Cover images: five images of size 512×512 – Yale Face Database • ORL database • The classification is executed using nearest neighbor (NN) with Euclidean distance. • Experiments conducted for two different scenarios: • Single training image • 50% Training and 50% testing • Two different block sizes, 3×3 and 5×5.
  36. 36. DWT LBP- Face Recognition - Accuracy • Recognition accuracy (%), Yale database, 50% training 50% testing, with 5x5 blocks 0 20 40 60 80 100 N8 N4 • Recognition accuracy (%), ORL database, 50% training 50% testing, with 5x5 blocks 0 20 40 60 80 100 N8 N4
  37. 37. Payload vs. imperceptibility • Number of bins used in each case • PSNR in different combination of sub-bands, each sub-band subdivided to 3 x 3 0 10 20 30 40 50 60 70 80 One Two Three Four Six N8 N4 0 2000 4000 6000 8000 10000 One Two Three Four Six 3 x 3 N8 3 x 3 N4 5 x 5 N8 5 x 5 N4
  38. 38. Robustness against LSB targeted steganalysis  None of the two most common steganalysis techniques PoV and RS can detect the LSB- witness stego-image even when using 100% of cover pixels for embedding.  The secret message is still extracted from LSB plane but the scheme does not change the LSB  This technique does not introduce any additional visual distortion to the cover image.
  39. 39. Hiding LBPHs in 8-bit pattern matching (2) 0 1 0 1 0 0 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 0 1 0 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 0 1 1 0 0 1 1 1 1 0 0 0 0 1 0 1 1 0 0 1 0 1 0 0 1 0 1 1 1 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 0 1 0 0 1 0 0 1 1 1 1 1 0 0 0 1 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1 1 0 1 1 1 0 0 1 0 1 0 1 0 1 0 0 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 1 0 0 … 1 0 1 1 0 0 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 0 … 0 0 1 Cover Image Face Image LBP Code LSB Plane Extract the LSB plane Works in 2 rounds. Matched first and then unmatched First embed the 8-bits of the biometric secret that have a match in the cover LSB plane, then the remaining 8-bits that have a counterpart of Hamming distance 1, so on
  40. 40. Block selection strategies  The number of blocks in a wavelet sub-band is much smaller than the number of blocks in the cover image.  This can be exploited for different purposes: randomly for more security, or according to best matching blocks for reduced changes.
  41. 41. Results (Invisibility in terms of PSNR) Payload Traditional LSB 8-bits Pattern 4-bits Pattern 22% 57.535 62.845 63.624 45% 54.536 59.407 60.223 68% 52.763 57.557 58.348 91% 51.523 56.339 57.178 100% 51.176 55.924 56.869 0 10 20 30 40 50 60 70 22% 45% 68% 91% 100% Traditional LSB 8-bits Pattern 4-bits Pattern
  42. 42. Results (Detectability) Weighted Steganalysis Payload Technique used Baboon Camera Man Lena Living Room Blonde Women 0 -0.026 -0.023 -0.017 -0.038 0.241 22% T. LSB 0.177 0.213 0.227 0.206 0.478 8-bits Pattern 0.003 0.034 0.061 0.046 0.400 4-bits Pattern 0.027 0.061 0.070 0.041 0.309 45% T. LSB 0.405 0.446 0.458 0.434 0.667 8-bits Pattern 0.102 0.152 0.153 0.160 0.457 4-bits Pattern 0.102 0.148 0.146 0.125 0.355 68% T. LSB 0.650 0.684 0.681 0.648 0.813 8-bits Pattern 0.236 0.252 0.242 0.249 0.499 4-bits Pattern 0.186 0.226 0.204 0.223 0.390 91% T. LSB 0.870 0.935 0.917 0.903 1.037 8-bits Pattern 0.348 0.367 0.349 0.350 0.554 4-bits Pattern 0.264 0.304 0.278 0.301 0.447 100% T. LSB 0.948 1.020 0.994 0.980 1.113 8-bits Pattern 0.391 0.402 0.377 0.382 0.574 4-bits Pattern 0.288 0.328 0.303 0.326 0.477
  43. 43. Robustness against LSB targeted steganalysis  The two most relevant targeted steganalysis tools of WS and DIH cannot detect the matching stego-images  The DIH cannot estimate embedding ratio for both pattern lengths. Even for full payload it returns a ration of around 0.27. i.e. the scheme is robust again DIH.  Similarly, even with full payload the WS tool estimates a payload of 6% for the 8-pattern matching algorithm and 22% for the 4-pattern matching algorithm.
  44. 44. Analysis  The embedded feature do not leak the original data  Experiments show that 83-85% of N8 patterns in secret biometric message match the 8-bits substrings of cover LSB plane. Also 86-88% of N4 patterns in the secret biometric match the 4-bits substrings of cover LSB plane.  High invisibility is due to minimal changes in cover image  For more security, partition the cover image into non- overlapping blocks, split the secret into substrings of the same length as the size of the image blocks, and finally apply the proposed method block by block.
  45. 45. Remote Face based Authentication – An outline  A number of different Remote authentication protocols can be designed to use the 8-bits (or 4-bits) pattern matching to hide the LBPH face features in an image.  Assume a bank has face LBPH template(s) of its customers  A customer initiates a transaction with the bank system from his mobile device. The bank responds with a code that may specify the blocks selection, or permutation of the bins  The customer respond by taking photo of his own face, extract the appropriate LBPH, select a cover image, and embed the LPBU as specified and send to the bank.  The bank check the customer identity.
  46. 46. Conclusions (Biometrics embedding) • LBP was applied on wavelet sub-bands and features were extracted from multiple wavelet sub-bands. • For single sub-band the recognition rates proposed is better when non-LL sub-bands • Using our proposed, in some cases when multi-subbands used, recognition rate is reduced slightly if compared with normal uniform LBP. • High imperceptibility is achieved • Number of features representing a face is reduced by approx. 72% if compared with the normal uniform LBP.
  47. 47. Applications Sender Receiver Cover Cover
  48. 48. Acknowledgement
  49. 49. Thank you for your attention. Questions? sabah.jassim@buckingham.ac.uk The End

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