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Table 1 Existing biohash inversion attacks

From: Practical security and privacy attacks against biometric hashing using sparse recovery

Method Assumptions Security Privacy
Multiply with the - Random projection Attack with biohash  
pseudo-inverse of matrix is available from estimated features:  
the random projection - Threshold is fixed - existing key  
matrix [17, 33] and it is 0 - a new key is assigned  
  - Wavelet FMT face and stolen again  
  features   
Genetic algorithms - Random projection 1) Attack with biohash  
[18] matrix is available from estimated features:  
  - Threshold is fixed - existing key  
  and it is 0 - a new key is assigned  
  - Fingercode features and stolen again  
   2) Average distance  
   between real and  
   approximated features  
Perceptron-learning - Several biohashes Identification scenario, Adversary has
with hill climbing and of various different where biohash generated access to output
MLP modeling with subjects are available from each synthetic face of feature extractor
customized hill- (other methods assume is matched against the given a face image
climbing [19] availability of a single stolen templates and applies hill-
  stolen biohash)   climbing attack to
  - Attacker can access   generate synthetic
  the matching scores of   face images
  the system   
  - Secret key of the   
  user is available   
Solve a constrained - Random projection Attack with biohash Reconstructed
minimization of matrix is available from estimated features: face images
distance between - Threshold is available - existing key from estimated
estimated features - A database of - a new key is assigned vector using
and unrelated unrelated features and stolen again PCA inversion
feature vector [4] - Eigenface features   
Methods proposed - Random projection 1) Attack with biohash Orthogonal linear
and discussed matrix is available from estimated features: face features
in this study: - Threshold is available - existing key (i.e., PCA, LDA):
  - Eigenface features - a new key is assigned transformation
- Sparse recovery   and is unknown matrix is known
- Min-norm solutions   - a new key is assigned and its inverse
   and stolen again is used to
   2) Verification accuracy reconstruct
   using the real features face images
   as gallery and  
   approximated features  
   as probe