Towards More Robust Commutative Watermarking-Encryption of Images

Schmitz, Roland; Li, Shujun; Grecos, Christos; Zhang, Xinpeng

doi:10.1109/ism.2013.54

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…The basic watermarking process closely follows the approach taken in [8] and [7] and is based on the idea of swapping histogram bins according to a secret watermarking key W K [3]: For each watermark bit w i ∈ {−1, 1}, the algorithm randomly selects a certain histogram bin a i and another bin b i within a dneighbourhood of a i , taking W K as initial seed. Here, d is a fixed parameter governing the tradeoff between robustness and transparency of the watermark.…”

Section: Watermarking Processmentioning

confidence: 99%

“…An image is deemed unauthentic, if the linear correlation is below a certain threshold T . Assuming a balanced distribution of the bipolar bits in w(I, c), the corresponding False Positive probability is [7]:…”

Section: Watermarking Processmentioning

confidence: 99%

“…In principle, any permutation of the histogram bins will produce an image version with the same pixel entropy as the original image. While most of these permutations will destroy the watermark because embedder and detector are de-synchronized, the watermarking process can be made robust against cyclic shifts by a suitable calibration of the embedding and detection process [7]. In a cyclic shift of the histogram, the grey values of the pixels in the three colour channels undergo the following transformation:…”

Section: Collision-resistant Versionmentioning

confidence: 99%

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Content-Fragile Commutative Watermarking-Encryption Based on Pixel Entropy

Schmitz

Grecos

et al. 2015

Advanced Concepts for Intelligent Vision Systems

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Abstract. Content-fragile commutative watermarking-encryption requires that both the content-fragile image signature and the watermarking process are invariant under encryption. The pixel entropy, being dependent on first-order image statistics only, is invariant under permutations. In the present paper we embed semi-fragile signatures based on pixel entropy by using a histogram-based watermarking algorithm, which is also invariant to permutations. We also show how the problem of collisions, i.e. different images having the same signature, can be overcome in this approach, if embedder and encryptor share a common secret.

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Section: Watermarking Processmentioning

confidence: 99%

Section: Watermarking Processmentioning

confidence: 99%

Section: Collision-resistant Versionmentioning

confidence: 99%

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Content-Fragile Commutative Watermarking-Encryption Based on Pixel Entropy

Schmitz

Grecos

et al. 2015

Advanced Concepts for Intelligent Vision Systems

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“…Compared with the encryption technique, the watermarking technique embeds the invisible information into the data, which can be extracted and used to authenticate data's ownership or identification, but it cannot ensure the security in data transmission [11]- [18]. Therefore, it is possible to overcome the limitation of a single technique by combining both the techniques of encryption and watermarking together to protect both confidentiality and ownership/ identification [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

Commutative Encryption and Watermarking Algorithm Based on Feature Invariants for Secure Vector Map

et al. 2020

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The existing commutative encryption and watermarking (CEW) methods based on feature invariants can achieve both the robustness of the watermarking algorithm and the security of the encryption algorithm. However, they are only applicable to the raster data such as images, videos, etc. In particular, the organization structure and storage structure of vector map have not been considered in these methods. Therefore, they cannot be used for vector map. This paper derives two feature invariants to solve this problem, which are the sum of inner angles and the storage direction of two adjacent objects according to the inherent characteristics of vector map. Based on these two feature invariants, a new CEW method is proposed in this paper, which includes the feature invariants based watermarking algorithm and the perceptual stream cipher based encryption algorithm on coordinates. Since the coordinate values used in encryption and the feature invariants used in watermarking are independent of each other, the commutativity is achieved for the proposed CEW method. The experiments are given to verify that the proposed CEW method can achieve the commutativity between encryption and watermarking without deteriorating accuracy of data. Besides, it has been verified that the proposed method is more robust to rotate, scaling, translation, and projection transformation compared with the existing CEW methods and has high security. The proposed algorithm has good scalability of encryption, and arbitrary encryption methods based on encrypting the coordinate values can be applied without affecting the extracted feature invariants.INDEX TERMS Commutative encryption and watermarking, feature invariant, vector map, perceptual stream cipher, lossless.

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“…To overcome this problem, in the present paper, we further improve our earlier work described in (Schmitz et al, 2012) by deploying a synchronization process between embedder and detector that is based on the global mean of the histogram. A preliminary edition of the present paper was presented at the 2013 IEEE International Symposium on Multimedia (ISM), see (Schmitz et al, 2013).…”

Section: Introductionmentioning

confidence: 99%