• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2008년도 정보통신설비 학술대회
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• pp.531-534
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• 2008

Image Hash specifies as a descriptor that can be used to measure similarity in images. Among all image Hash methods, histogram based image Hash has robustness to common noise-like operation and various geometric except histogram _equalization. In this_paper an improved histogram based Image Hash that is using "Imadjust" filter I together is proposed. This paper has achieved a satisfactory performance level on histogram equalization as well as geometric deformation.

• 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.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2008년도 추계학술대회
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• pp.15-19
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• 2008

Image hash는 영상에서 유사성을 찾는 방법으로 사용될 수 있는 기술자(Descriptor)로 특징지을 수 있다. 많은 image hash 방법중에 Histogram-based image hash는 Histogram equalization을 제외한 보통 잡음 및 다양한 기하학적 변조를 주어도 같은 그림을 찾아내는데 강력한 기능을 수행한다. 본 논문에서는 Histogram-Based Hash를 생성함에 있어 서로 다른 3개의 bin의 관계를 이용하여 Hash를 생성하였다. 본 논문은 이를 통해 영상의 유사성을 찾아내는데 있어 원본영상에 대해 기하학적 변조뿐만 아니라 상대적으로 성능이 약했던 Histogram equalization을 이용한 변조에 대해서도 성능이 개선되었다. 또한 가우시안 필터링의 알파 값을 다르게 지정함으로 인하여 생성되는 두 히스토그램을 이용하여 기존의 방법보다 성능이 개선되었다.

• 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.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2009년도 정보통신설비 학술대회
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• pp.143-146
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• 2009

There are some image hash methods, in the paper four image hash methods have been compared: FJLT (Fast Johnson- Lindenstrauss Transform), SVD (Singular Value Decomposition), NMF (Non-Negative Matrix Factorization), FP (Feature Point). From the compared result, FJLT method can't be used in the online. the search time is very slow because of the KNN algorithm. So FJLT method has been improved in the paper.

• 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.

• 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.

• 전기전자학회논문지
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• 제14권4호
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• pp.339-345
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• 2010

보다 빠른 내용 기반 이미지 검색을 위해, 다차원 특징 정보의 효과적인 인덱싱에 대한 다양한 연구들이 수행되고 있다. 하지만, 대부분의 인덱싱 기법들은 특징 정보의 차원이 커질수록 성능이 저하되는 문제를 가지고 있으며, 이를 대체하기 위해서 '높은 확률'로써 사용자가 원하는 결과를 제공해 주기 위한 휴리스틱 (heuristic) 알고리즘을 사용한 기법들이 제안되었다. 본 논문에서는 이러한 다차원 특징 정보를 효과적으로 인덱싱 하기 위해, 연장 해쉬 기반의 새로운 인덱싱 기법을 제안한다. 제안된 인덱싱 기법은 기존의 기법들이 가졌던 문제들을 해결하기 위해, 검색의 정확도에 영향을 주지 않으면서 빠른 검색이 가능하도록 설계되었다. 다양한 실험을 통해, 제안된 기법이 월등한 성능을 가질 수 있음을 보였다.

• 정보학연구
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• 제9권4호
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• pp.49-55
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• 2006

In this paper, a new fragile watermarking algorithm for digital image is presented, which makes resolving the security and forgery problem of the digital image to be possible. The most suitable watermarking method that verifies the authentication and integrity of the digital image is the Wong's method, which invokes the hash function (MD5). The algorithm is safe because this method uses the hash function of the cryptology. The operations such as modulus, complement, shift, bitwise exclusive-or, bitwise inclusive-or are necessary for calculating the value of hash function. But, in this paper, an Arithmetic encoding method that only includes the multiplication operation is adopted. This technique prints out accumulative probability interval, which is obtained by multiplying the input symbol probability interval. In this paper, the initial probability interval is determined according to the value of the key, and the input sequence of the symbols is adjusted according to the key value so that the accumulative probability interval will depend on the key value. The integrity of the algorithm has been verified by experiment. The PSNR is above the 51.13db and the verifying time is $1/3{\sim}1/4$ of the verifying time of using the hash function (MD5), so, it can be used in the real-time system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1448-1463
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• 2021

With the continuous development of cloud storage, plenty of redundant data exists in cloud storage, especially multimedia data such as images and videos. Data deduplication is a data reduction technology that significantly reduces storage requirements and increases bandwidth efficiency. To ensure data security, users typically encrypt data before uploading it. However, there is a contradiction between data encryption and deduplication. Existing deduplication methods for regular files cannot be applied to image deduplication because images need to be detected based on visual content. In this paper, we propose a secure image deduplication scheme based on hashing and clustering, which combines a novel perceptual hash algorithm based on Local Binary Pattern. In this scheme, the hash value of the image is used as the fingerprint to perform deduplication, and the image is transmitted in an encrypted form. Images are clustered to reduce the time complexity of deduplication. The proposed scheme can ensure the security of images and improve deduplication accuracy. The comparison with other image deduplication schemes demonstrates that our scheme has somewhat better performance.