Tamper Detection and Localization for Categorical Data Using Fragile Watermarks - PowerPoint PPT Presentation

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Tamper Detection and Localization for Categorical Data Using Fragile Watermarks

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A digital watermark is technique of embedding an invisible signal directly into ... 1: sort tuples in Gk in ascendant order according to their primary ... – PowerPoint PPT presentation

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Title: Tamper Detection and Localization for Categorical Data Using Fragile Watermarks


1
Tamper Detection and Localization for Categorical
DataUsing Fragile Watermarks
2
What is Digital watermarking?
  • A digital watermark is technique of embedding an
    invisible signal directly into the data, thus
    providing a promising way to protect digital data
    from illicit copying and manipulation.

3
Why is it required?
  • It is used to detect data tampering and illicit
    copying of data.
  • It complements cryptography and steganography.
  • Cryptography- once the encrypted data is
    decrypted, the data is clear and no longer under
    protection.
  • Steganography The problem is that it cannot
    extract the hidden data if the stego data undergo
    some distortions.

4
Applications
  • Copy protection
  • Authentication
  • Tamper detection

5
Classifications of Digital Watermarks
  • Fragile watermarks for tamper detection
  • Robust watermarks for ownership verification

6
Difference between multimedia and Database
Watermarking
  • Multimedia data are highly correlated, there is a
    lot of redundant information present in
    multimedia data.
  • Database relations contain large number of
    independent tuples and all tuples are equally
    important.

7
Challenges
  • Embedding watermarks in database relations is a
    challenging problem because there is little
    redundancy present in a database relation.

8
Watermark Embedding Algorithm
  • Algorithm 1 Watermark embedding
  • 1 For all k ? 1, g qk 0
  • 2 for i 1 to ? do
  • 3 hi HASH(K, ri.A1, ri.A2,
    , ri.A?) // row hash
  • 4 hpi HASH(K, ri.P ) // primary
    key hash
  • 5 k hpi mod g
  • 6 ri ? Gk
  • 7 qk
  • 8 end for
  • 9 for k 1 to g do
  • 10 watermark embedding in Gk // See
    Algorithm 2
  • 11 end for

9
Watermark Embedding Algorithm(cont)
  • Algorithm 2 Watermark embedding in Gk
  • sort tuples in Gk in ascendant order according
    to their primary
  • key hash // Virtual operation
  • H HASH(K, h1, h2, , hqk)
  • W extractBits(H, qk/2) // See line 9 - 16
  • for i 1, i lt qk, i i 2 do
  • if (Wi/2 1 and hi lt hi1) or (Wi/2
    0 and hi gt hi1) then
  • switch the position of ri and ri1
  • end if
  • end for
  • extractBits(H, l) 10 if length(H) l then
  • W concatenation of first l selected bits from
    H
  • else
  • m l - length(H)
  • W concatenation of H and extractBits(H,m)
  • end if
  • return W

10
Example
  • Figure
    1 A table before and after watermark embedding

WM0,1
11
Watermark Detection
  • Algorithm 3 Watermark detection
  • 1 For all k ? 1, g qk 0
  • 2 for i 1 to ? do
  • 3 hi HASH(K, ri.A1, ri.A2, , ri.A?) //
    tuple hash
  • 4 hp
  • i HASH(K, ri.P ) // primary key hash
  • 5 k hp
  • i mod g
  • 6 ri ? Gk
  • 7 qk
  • 8 end for
  • 9 for k 1 to g do
  • 10 watermark verification in Gk // See Algorithm
    4
  • 11 end for

12
Watermark Detection
  • Algorithm 4 Watermark verification in Gk
  • 1 sort tuples in Gk in ascendant order according
    to their primary
  • key hash // Virtual operation
  • 2 H HASH(K, h1, h2, , hqk) // hi(i 1,
    qk) is the
  • tuple hash of ith tuple after ordering
  • 3 W extractBits(H, qk/2) //See line 9-16 in
    Algorithm 2
  • 4 for i 1, i lt qk, i i 2 do
  • 5 if hi hi1 then
  • 6 Wi/2 0
  • 7 else
  • 8 Wi/2 1
  • 9 end if
  • 10 end for
  • 11 ifW W then
  • 12 V TRUE
  • 13 else
  • 14 V FALSE
  • 15 end if
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