• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권12호
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• pp.5058-5072
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• 2015

Quaternions have been commonly employed in color image processing, but when the existing pure quaternion representation for color images is used in perceptual hashing, it would degrade the robustness performance since it is sensitive to image manipulations. To improve the robustness in color image perceptual hashing, in this paper a full quaternion representation for color images is proposed by introducing the local image luminance variances. Based on this new representation, a novel Full Quaternion Discrete Cosine Transform (FQDCT)-based hashing is proposed, in which the Quaternion Discrete Cosine Transform (QDCT) is applied to the pseudo-randomly selected regions of the novel full quaternion image to construct two feature matrices. A new hash value in binary is generated from these two matrices. Our experimental results have validated the robustness improvement brought by the proposed full quaternion representation and demonstrated that better performance can be achieved in the proposed FQDCT-based hashing than that in other notable quaternion-based hashing schemes in terms of robustness and discriminability.

• Journal of Information Processing Systems
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• 제19권6호
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• pp.756-766
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• 2023

To effectively track the illegal use of digital images and maintain the security of digital image communication on the Internet, this paper proposes a reversible multipurpose image watermarking algorithm based on a deep residual network (ResNet) and perceptual hashing (also called MWR). The algorithm first combines perceptual image hashing to generate a digital fingerprint that depends on the user's identity information and image characteristics. Then it embeds the removable visible watermark and digital fingerprint in two different regions of the orthogonal separation of the image. The embedding strength of the digital fingerprint is computed using ResNet. Because of the embedding of the removable visible watermark, the conflict between the copyright notice and the user's browsing is balanced. Moreover, image authentication and traitor tracking are realized through digital fingerprint insertion. The experiments show that the scheme has good visual transparency and watermark visibility. The use of chaotic mapping in the visible watermark insertion process enhances the security of the multipurpose watermark scheme, and unauthorized users without correct keys cannot effectively remove the visible watermark.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3790-3803
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• 2018

Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.93-96
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• 2021

The desire for a technology that can mechanically acquire 2D images starting with the manual method of drawing has been making possible a wide range of modern image-based technologies and applications over a period. Moreover, this trend of the utilization of image-related technology as well as image-based information is likely to continue. Naturally, as like other technology areas, the function that humans produce and utilize by using images needs to be automated by using computing-based technologies. Surprisingly, technology using images in the future will be able to discover knowledge that humans have never known before through the information-related process that enables new perception, far beyond the scope of use that human has used before. Regarding this trend, the manipulation and configuration of massively distributed image database system is strongly demanded. In this paper, we discuss image identifier production methods based on the utilization of the image hashing technique which especially puts emphasis over an entropy operator.

• 한국멀티미디어학회논문지
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• 제20권10호
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• pp.1619-1627
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• 2017

Image hashing has been successfully applied for the problems associated with the protection of intellectual property, management of large database and indexation of content. For a reliable hashing system, improving hash matching accuracy is crucial. In order to improve the hash matching performance, we propose an asymmetric hash matching method using the psychovisual threshold, which is the maximum amount of distortion that still allows the human visual system to identity an image. A performance evaluation over sets of image distortions shows that the proposed asymmetric matching method effectively improves the hash matching performance as compared with the conventional Hamming distance.

• 한국통신학회논문지
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• 제35권12C호
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• pp.995-1003
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• 2010

영상, 동영상 및 3D 모델의 인증 및 복사방지를 위한 콘텐츠 기반 해쉬 함수는 강인성 및 보안성의 성질을 만족하여야 한다. 이들 중 해쉬의 보안성을 분석하기 위한 방법으로 미분 엔트로피 방법이 제시되었으나, 이는 영상 해쉬 추출에서만 적용되었다. 따라서 본 논문에서는 미분 엔트로피 기반의 3D 모델 해쉬 특징 추출의 보안성을 분석하기 위한 모델링을 제안한다. 제안한 보안성 분석 모델링에서는 3D 모델 해싱 기법 중 가장 일반적인 두 가지 형태의 특정 추출 방법을 제시한 다음, 이들 방법들을 미분 엔트로피 기반으로 보안성을 분석하였다. 과로부터 해쉬 추출 방법에 대한 보안성을 분석하고 보안성과 강인성과의 상호보완관계에 대하여 논하였다.

• International Journal of Advanced Culture Technology
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• 제5권1호
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• pp.76-82
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• 2017

An image hash, a discriminative and robust summary of an image, should be robust against quality-preserving signal processing steps, while being pairwise independent for perceptually different inputs. In order to improve the hash matching performance, this paper proposes a probabilistic dissimilarity matching. Instead of extracting the binary hash from the query image, we compute the probability that the intermediate hash vector of the query image belongs to each quantization bin, which is referred to as soft quantization binning. The probability is used as a weight in comparing the binary hash of the query with that stored in a database. A performance evaluation over sets of image distortions shows that the proposed probabilistic matching method effectively improves the hash matching performance as compared with the conventional Hamming distance.

• Journal of Computing Science and Engineering
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• 제11권1호
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• pp.1-8
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• 2017

Naive Bayes nearest neighbor (NBNN) is a simple image classifier based on identifying nearest neighbors. NBNN uses original image descriptors (e.g., SIFTs) without vector quantization for preserving the discriminative power of descriptors and has a powerful generalization characteristic. However, it has a distinct disadvantage. Its memory requirement can be prohibitively high while processing a large amount of data. To deal with this problem, we apply a spherical hashing binary code embedding technique, to compactly encode data without significantly losing classification accuracy. We also propose using an inverted index to identify nearest neighbors among binarized image descriptors. To demonstrate the benefits of our method, we apply our method to two existing NBNN techniques with an image dataset. By using 64 bit length, we are able to reduce memory 16 times with higher runtime performance and no significant loss of classification accuracy. This result is achieved by our compact encoding scheme for image descriptors without losing much information from original image descriptors.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2011년도 추계학술대회
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• pp.364-365
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• 2011

In this paper, we show that methods of perceptual image hashing which have been proposed recent years. And the disadvantages of them. Perceptual robustness, security and fragility are properties what we always discuss. Then we propose some ideas which we will do the research later.

• 전자공학회논문지CI
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• 제48권1호
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• pp.133-140
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• 2011

최근 들어, 웹 응용의 하나로 이미지를 통합 관리하는 이미지 거래소(Image Stock), 이미지 도서관(Image Library)과 같은 응용들이 많이 만들어 지고 있다. 이미지의 등록, 관리, 검색에는 주로 이미지 해쉬라는 기술이 구분자(Identifier)로서 쓰이며 해쉬의 분별력을 높이기 위한 연구들이 많이 진행되어지고 있다. 본 논문에서는 계층적 히스토그램을 이용한 GLOCAL(Global to Local) 이미지 해쉬 생성 방법을 제안하였다. 많은 연구들이 이미지 처리 및 기하학적 공격에 강한 히스토그램 기반의 이미지 해쉬 기법들을 제안하였으며 제안된 논문에서는 GLOCAL 해쉬 생성과 가중치(Weighting Factor)를 적용하여 해쉬의 안정성을 높이는데 기여하였다. GLOCAL 해쉬 생성 방법에 의해 기존의 알고리즘들은 좀더 풍부한 길이의 이미지 해쉬를 생성하였다. 즉, 이미지 해쉬의 근본 목적인 Identification과 Discrimination 이라는 두 가지 목적을 잘 달성하였으며 그 결과는 통계학적 가설 검정 (Statistical Hypothesis Testing)을 통해 기존의 알고리즘과 비교하였으며 대부분의 공격종류에 대해 제안된 알고리즘이 향상된 성능을 보여줌을 확인하였다.