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Accusation probabilities in Tardos codes. Antonino Simone and Boris Š kori ć Eindhoven University of Technology WISSec 2010, Nov 2010. Outline. Introduction to forensic watermarking Collusion attacks Aim Tardos scheme q- ary version Properties Performance of the Tardos scheme
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Accusation probabilities in Tardos codes Antonino Simone and Boris Škorić Eindhoven University of Technology WISSec 2010, Nov 2010
Outline • Introduction to forensic watermarking • Collusion attacks • Aim • Tardos scheme • q-ary version • Properties • Performance of the Tardos scheme • False accusation probability • Results & Summary
originalcontent originalcontent content withhidden payload payload WM secrets payload WM secrets Detector Embedder Forensic Watermarking ATTACK Payload = some secret code indentifying the recipient
"Coalition of pirates" = "detectable positions" pirate #1 1 1 1 0 1 0 1 0 0 0 0 1 1 0 1 0 1 0 1 0 1 0 1 1 #2 1 0 1 0 1 0 1 0 0 0 1 1 #3 1 1 1 0 0 0 1 1 0 0 0 1 #4 AttackedContent 1 0/1 1 0 0/1 0 1 0/1 0/1 0 0/1 1 Collusion attacks
Aim Trace at least one pirate from detected watermark BUT Resist large coalition longer code Low probability of innocent accusation (FP) (critical!) longer code Low probability of missing all pirates (FN) (not critical) longer code AND Limited bandwidth available for watermarking code
q-aryTardos scheme (2008) m content segments biases Symbol biases drawn from distribution F embedded symbols • Arbitrary alphabet size q • Dirichletdistribution F n users c pirates Symbols allowed =y watermark after attack
Tardos scheme continued • Accusation: • Every user gets a score • User is accused if score > threshold • Sum of scores per content segment • Given that pirates have y in segment i: • Symbol-symmetric
Properties of the Tardos scheme • Asymptotically optimal • m c2 for large coalitions, for every q • Previously best m c4 • Proven: power ≥ 2 • Random code book • No framing • No risk to accuse innocent users if coalition is larger than anticipated • F, g0 and g1 chosen ‘ad hoc’ (can still be improved)
Accusation probabilities Result: majority voting minimizes u m = code length c = #pirates u = avg guilty score Pirates want to minimize u and make longer the innocent tail threshold • Curve shapes depend on: • F, g0, g1 (fixed ‘a priori’) • Code length • # pirates • Pirate strategy guilty innocent u total score (scaled) Central Limit Theorem asymptotically Gaussian shape (how fast?) 2003 2010: innocent accusation curve shape unknown… till now!
Approach Fourier transform property: • Steps: • S = iSi • Si • = pdf of total score S • S = InverseFourier[ ] • Compute • Depends on strategy • New parameterization for attack strategy • Compute • Taylor • Taylor • Taylor
Main result: false accusation probability curve threshold/√m Example: exact FP majority voting attack Result from Gaussian log10FP FP is 70 times less than Gaussian approx in this example But Code 2-5% shorter than predicted by Gaussian approx
Summary Results: • introduced a new parameterization of the attack strategy • majority voting minimizes u • first to compute the innocent score pdf • quantified how close FP probability is to Gaussian • sometimes better then Gaussian! • safe to use Gaussian approx Future work: • study more general attacks • different parameter choices Thank you for your attention!