Performance of Orthogonal Fingerprinting Codes Under Worst-Case Noise project is developed in programming language. Detailed explanation of project is given below.

Performance of Orthogonal Fingerprinting Codes Under Worst-Case Noise

Existing System

Problem of great theoretical and practical interest is to know what is the worst collusion attack, subject to a maximum  distortion constraint on the illegal copy. This question has been addressed in capacity and error-exponent analysis for fingerprints defined over finite alphabets. Depending on the problem setup, the worst collusion channel is either a memoryless or a “nearly memoryless” multiple-access channel that can be identified as the solution to a communication game. For fingerprints and signals defined over Euclidean spaces, the worst collusion channel subject to mean-squared distortion constraints was identified in the capacity analysis.


This project has four main modules namely 

  • Registration module
  • Finger print embedding module
  • Collision attack module
  • Authentication module
  • Verification module 

Module Description 

  • Registration module 

In this module user have enter his personal details . In this module it will collect the user login details. The details will used for his future authentication process Our fingerprints form a randomized orthogonal code, where the randomization parameter is a rotation. The noiseless forgery is obtained by uniform linear averaging of the colluders’ copies. The detector has access to the host signal and performs a binary hypothesis test to verify whether a user of interest is colluding. The cost function in this problem is the detector’s error probability. 

  • Finger print embedding module 

In this module the user will act according to the attackers actions. In this projects the user during registration time he will used to embedded his fingerprint information according to his value and store it into the databse specific field. But the original fingerprint will be stored at the back of dupilicate fingerprint with embedding . so this makes the the user data to be in safer side.