Presentation on theme: "Digital Watermarking for Telltale Tamper Proofing and Authentication Deepa Kundur, Dimitrios Hatzinakos Presentation by Kin-chung Wong."— Presentation transcript:
Digital Watermarking for Telltale Tamper Proofing and Authentication Deepa Kundur, Dimitrios Hatzinakos Presentation by Kin-chung Wong
Robust Watermark Able to recover embedded message from media even when attacked Application: –Authenticates content source –Digital rights management (DRM) –Embed secret message (steganography)
Fragile Watermark Embedded in such a way that, when the media is modified, the watermark tells something about the nature and strength of modification Application: tamper-proofing for court evidence, certificates, financial documents
Requirements 1.Indicate whether distortion exists. 2.Indicate relative magnitude of distortion 3.Characterize type of distortion, especially distinguishing between compression and intentional tampering 4.Validate and authenticate without generating metadata (non-image data sent along with an image file) … (*) (*) Uninformed users need not pass along metadata with the image. However, decoder still needs additional information from encoder, including original watermark bit sequence.
Requirements in details The watermark can be extracted from watermarked media (z) without explicit knowledge of host (f). Difference between w and provide information on signal modification, in terms of nature and strength of distortion User decides whether to accept content as authentic, based on the above information
Choice of embedding domain Discrete wavelet transform For N-bit watermark, select N coefficients in the wavelet domain, and embed one bit per coefficient using quantization index modulation Advantage: tampering is detected with both spatial and frequency localization by observing where errors occur most
Encoder Host DWT: Watermark: Coefficient selection key: Watermark quantization parameter (*): (*) different from, but related to coefficient rounding of file format
Decoder Extraction: get at the coefficient indicated by ckey(i) Tamper assessment function: (TAF) = (Number of bits flipped) / (Total number of bits)
Embedding one bit in a coefficient Quantized “bin” function Original wavelet coef.
Noise analysis Mild distortion –Small additive Gaussian noise – small, we can take approximations Severe distortion –Extracted watermark bits become unpredictable –Heavy filtering, least-significant-bit truncation, image region substitution, geometrical distortion –No correlation between w(i) and
Mild distortion Red: Changed but not detected Green: Changed and detected