Secure and Robust Two-Phase Image Authentication

Tabatabaei, Seyed Amir Hossein; Ur-Rehman, Obaid; Živić, Nataša; Ruland, Christoph

doi:10.1109/tmm.2015.2432672

Cited by 25 publications

(10 citation statements)

References 30 publications

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“…Here, h is the high quantization level and l is the low quantization level. A bitmap (bm) for the current block is generated based on the following rules: (1) If the pixel belongs to the subgroup, G S0 , its corresponding position in the bitmap is marked '0'; (2) if the pixel belongs to the subgroup, G S1 , its corresponding position in the bitmap is marked '1'. The generation rule for the bitmap is described as follows:…”

Section: Ambtc Compressionmentioning

confidence: 99%

“…Step 4. Extract the first seven bits of bm as the first cover vector, CV 1 = cv 1 1 , cv 1 2 , cv 1 3 , cv 1 4 , cv 1 5 , cv 1 6 , cv 1 7 = (bm (1,1) , bm (1,2) , bm (1,3) , bm (1,4) , bm (2,1) , bm (2,2) , bm (2,3) ). Extract the last seven bits of bm as the second cover vector, CV 2 = cv 2 1 , cv 2 2 , cv 2 3 , cv 2 4 , cv 2 5 , cv 2 6 , cv 2 7 = (bm (3,2) , bm (3,3) , bm (3,4) , bm (4, 1) , bm (4,2) , bm (4,3) , bm (4,4) ).…”

Section: The Matrix Encoding Based Data Hidingmentioning

confidence: 99%

“…where a, b ∈ [1,2], h a ∈ [h 1 , h 2 ], l b ∈ [l 1 , l 2 ]. Besides, in a few cases, this situation may happen, that is, h cf will be equal to l cf , then h cf or l cf will be forced to be justified once again by the following rule:…”

Section: Adjusted Quantization Levels Matching Based Data Hidingmentioning

confidence: 99%

“…Researchers have conducted a series of scientific studies on image authentication. Roughly, authentication methods can be classified into two categories: Digital signature-based methods [1][2][3][4] and digital watermark-based methods . Generally, the digital signature-based method performs the encryption of the hashed results of the features of the image using a private key to form a unique signature, which will be used later for authentication.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

High-Precision Authentication Scheme Based on Matrix Encoding for AMBTC-Compressed Images

Chang

Lin

2019

Symmetry

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In this paper, a high-precision image authentication scheme for absolute moment block truncation coding (AMBTC)-compressed images is presented. For each block, two sub-bitmaps are conducted using the symmetrical separation, and the six-bit authentication code is symmetrically assigned to two sub-codes, which is virtually embedded into sub-bitmaps using the matrix encoding later. To overcome distortion caused by modifications to the bitmap, the corresponding to-be-flipped bit-location information is recorded instead of flipping these bits of the bitmap directly. Then, the bit-location information is inserted into quantization levels based on adjusted quantization level matching. In contrast to previous studies, the proposed scheme offers a significantly improved tampering detection ability, especially in the first hierarchical tampering detection without remediation measures, with an average tampering detection rate of up to 98.55%. Experimental results show that our approach provides a more stable and reliable tampering detection performance and sustains an acceptable visual quality.

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Section: Ambtc Compressionmentioning

confidence: 99%

Section: The Matrix Encoding Based Data Hidingmentioning

confidence: 99%

Section: Adjusted Quantization Levels Matching Based Data Hidingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-Precision Authentication Scheme Based on Matrix Encoding for AMBTC-Compressed Images

Chang

Lin

2019

Symmetry

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show abstract

“…So far, several identification schemes and image hash functions have been developed for authenticating images [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. They can be broadly classified into two types according to a difference in extracted features: compression method-depended type, to which the proposed scheme corresponds, and compression methodindependent type.…”

Section: Introductionmentioning

confidence: 99%

[Paper] Fuzzy Commitment Scheme-Based Secure Identification for JPEG XR Images under Various Compression Ratios

Iida

Kiya

2017

MTA

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A secure identification scheme for JPEG XR images is proposed in this paper.The aim is to securely identify JPEG XR images which are generated from the same original image under various compression levels. The positive and negative signs of lapped biorthogonal transform coefficients are used as features.The proposed scheme is robust against a difference in compression levels, and does not produce false negative matches in any compression level. In addition, to construct a secure identification system, we propose a novel identification system that consists of an error correction technique and a fuzzy commitment scheme, which is a well-known biometric cryptosystem. There is no existing scheme having these properties for JPEG XR images. Moreover, a way for speeding up the identification is also proposed. The experimental results show the proposed scheme is effective for identifying JPEG XR images in terms of true positive matches.

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Twizzle - A Multi-purpose Benchmarking Framework for Semantic Comparisons of Multimedia Object Pairs

et al. 2020

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Secure and Robust Two-Phase Image Authentication

Cited by 25 publications

References 30 publications

High-Precision Authentication Scheme Based on Matrix Encoding for AMBTC-Compressed Images

High-Precision Authentication Scheme Based on Matrix Encoding for AMBTC-Compressed Images

[Paper] Fuzzy Commitment Scheme-Based Secure Identification for JPEG XR Images under Various Compression Ratios

Twizzle - A Multi-purpose Benchmarking Framework for Semantic Comparisons of Multimedia Object Pairs

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