Fault Resilient and Compression Tolerant Digital Signature for Image Authentication Der-Chyuan Lou and Jiang-Lung Liu, IEEE Transactions on Consumer Electronics, Vol. 46, No. 1,February 2000, pp.31-39 Advisor ：Dr. Chang, Chin-Chen Reporter：Lee, Jiau-Yun Date ：2003/3/25

Outline Introduction The Proposed Method Feature Extraction Feature Comparison Image Recovery Experimental Results Conclusions

Introduction Image authentication is becoming important. The study categories of Image authentication: Digital watermark---sellers Digital signature---buyers The criteria of fault resilient and compression tolerant scheme.

The Criteria of Fault Resilient and Compression Tolerant Scheme Any legal receiver can authenticate the ownership. It should be detected when the image is tampered. The modified portion should be located. The hash result should be small and robust.

The Proposed Method Digital signature generation: Feature Original Extraction Encryption Original Image Digital Signature Sender’s Secret Key

The Proposed Method(cont.) Verification and recovery: Received Image Decompression Feature Extraction Feature Comparison Digital Signature Decryption Feature Reversing Sender’s Public Key Image Recovery

Feature Extraction---Steps Original Image Block-means computation Block-means Quantization Encoding Image Feature

Feature Extraction---Steps(cont.) Steps can be formulated as: Original map: Io Block-means map: IM Quantized means map: IQ Encoded map: IE Transformation function: f

Feature Extraction---threshold Block-means Quantization Original Image Block-means computation Encoding Image Feature Compression & Decompression Corrupted Image Block-means Quantization Difference Computation threshold Compression ration

Feature Comparison The procedure of feature comparison: 1.Create a temporary map of the same size as feature map and assign white color. 2.Find the difference between two features. 3.If the difference is large than the threshold, then mark it with black 4.If no black-marked in the temporary map, it is authentic.

Image Recovery Replace the intensity of black-marked blocks. There will possible exist some edges between blocks. Smoothing algorithms: 8 neighbor blocks must be recovered while the black-marked block is recovered.

Experimental Results (a)original image (b)map of block-means (c)quantized (256*256) (32*32) block-means(32*32)

Experimental Results(cont.) a tampered image by removing “U.S. AIR Force” the detected result. recovered image by using received image-feature. PSNR=31.6718dB retransmission 5% PSNR=79.8611dB

Conclusions The proposed image-features are compression tolerant. The threshold for compression tolerant property is user-defined. The proposed image-features can recover the modified portions.