Perceptual Image Hashing with Weighted DWT Features for Reduced-Reference Image Quality Assessment

Tang, Zhenjun; Huang, Ziqing; Yao, Heng; Zhang, Xianquan; Lv, Chen; Yu, Chunqiang

doi:10.1093/comjnl/bxy047

Cited by 41 publications

(30 citation statements)

References 27 publications

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“…To illustrate the advantage, our hashing is compared with some baseline hashing algorithms. The selected hashing algorithms include invariant moments‐based hashing [19], random walk‐based hashing [26], weighted DWT features‐based hashing [10], CVA‐DWT hashing [22] and hybrid features‐based hashing [32]. The image datasets described in Sections 3.1 and 3.2 are also used to validate robustness and discrimination of the compared algorithms.…”

Section: Performance Comparisonsmentioning

confidence: 99%

“…Image hashing can efficiently process image data due to the low costs of hash storage and hash similarity. Besides image copy detection, it has been successfully applied to many applications, such as copy‐move forgery detection [5, 6], image forensics [7], image authentication [8, 9], image quality assessment [10], social event detection [11] and tamper detection [12]. In the literature, many useful techniques are exploited to design efficient image hashing algorithms for various applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust image hashing with visual attention model and invariant moments

Tang

Zhang

Pun³

et al. 2020

IET Image Processing

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Section: Performance Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust image hashing with visual attention model and invariant moments

Tang

Zhang

Pun³

et al. 2020

IET Image Processing

Self Cite

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“…Author Zhenjun Tang et alin their work [2] has proposed a new approach to image hashing. Their basis was statistical weighted discrete wavelet transform (DWT) features.…”

Section: Part-a Literature About Some Image Hashing Techniquesmentioning

confidence: 99%

“…With 2 DDWT it is compressed .The Image hash has been obtained in LL sub band DWT coefficient concatenations. This paper [7] presented perceptual texture and structure features hashing method. The author followed the approach like, after preprocessing step the secondary image dual-cross pattern (DCP) is encoded and thus 2 coded map is generated.…”

Section: Part-a Literature About Some Image Hashing Techniquesmentioning

confidence: 99%

Implementation of Novel and Secured Parking Lot System Using Image Hashing and Matlab

Mohammadi¹,

Ahmed²,

Ansari³

et al. 2020

International Journal of Engineering Research and Technology

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This paper proposes a management and implementation of Novel parking system in two folds, first based on cameras scanned image of parking lot and second Image Hashing technique for image or data security itself. As the parking problem become one of the key issue of concern to car drivers or motorists, it must be addressed in a good time saving way and secured way to give smooth or comfortable feel to the motorists as well as to the society as a whole. Our system will help motorists to know before entering in parking if there is a place available for their cars or not by show current parking status of parking places beforehand. Information of whether there is a parking free or not and how many parking are free at that point of time with its rowcolumn address. The security to the system is provided by hashing algorithm. We used MATLAB software simulation to enable us to use different image prepossessing methods like detecting and tracking objects using separate background color from the other color objects by using Raspberry Pi microcontroller, Camera, module board, SD card that will increase rate of reliability and will decrease the cost. The Image hashing algorithms EfficientImageHashkeyGenerator will provide authenticity and security. Thus the proposed system will be secure system and support different operating Systems appropriate for various environments.

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“…Karsh et al [16] propose an image hashing scheme by using DWT-SVD and spectral residual method. Tang et al [17] divide an image into nonoverlapping blocks and apply a three-level two-dimensional DWT to each block to extract DWT statistical features.…”

Section: Related Workmentioning

confidence: 99%

SSL: A Novel Image Hashing Technique Using SIFT Keypoints with Saliency Detection and LBP Feature Extraction against Combinatorial Manipulations

Xue

Yuan

Liu

et al. 2019

Security and Communication Networks

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Image hashing schemes have been widely used in content authentication, image retrieval, and digital forensic. In this paper, a novel image hashing algorithm (SSL) by incorporating the most stable keypoints and local region features is proposed, which is robust against various content-preserving manipulations, even multiple combinatorial manipulations. The proposed algorithm combines S_cale invariant feature transform (SIFT) with S_aliency detection to extract the most stable keypoints. Then, the L_ocal binary pattern (LBP) feature extraction method is exploited to generate local region features based on these keypoints. After that, the information of keypoints and local region features are merged into a hash vector. Finally, a secret key is used to randomize the hash vector, which can prevent attackers from forging the image and the hash value. Experimental results demonstrate that the proposed hashing algorithm can identify visually similar images which are under both single and combinatorial content-preserving manipulations, even multiple combinations of manipulations. It can also identify maliciously forged images which are under various content-changing manipulations. The collision probability between hashes of different images is nearly zero. Besides, the evaluation of key-dependent security shows that the proposed scheme is secure that an attacker cannot forge or estimate the correct hash value without the knowledge of the secret key.

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Perceptual Image Hashing with Weighted DWT Features for Reduced-Reference Image Quality Assessment

Cited by 41 publications

References 27 publications

Robust image hashing with visual attention model and invariant moments

Robust image hashing with visual attention model and invariant moments

Implementation of Novel and Secured Parking Lot System Using Image Hashing and Matlab

SSL: A Novel Image Hashing Technique Using SIFT Keypoints with Saliency Detection and LBP Feature Extraction against Combinatorial Manipulations

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