A Robust and Discriminative Image Perceptual Hash Algorithm

Wen, Zhenkun; Wei-zong, Zhu; Ouyang-Jie,; Liu, Pengfei; Du, Yang; Zhang, Meng; Gao, Jin-hua

doi:10.1109/icgec.2010.180

Cited by 14 publications

(3 citation statements)

References 9 publications

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“…This property is termed as robustness in image perceptual hashing, which means that the hash algorithm should result in the same output bit string for images with the same underlying content. E.g., the raw image, its noisy version, compressed version, modified brightness version, modified contrast version, and its rotated version have the same underlying content and should share the same hash value [18]. Another property of image perceptual hashing is discrimination which implies that the hash values for any two distinct images should be different and random.…”

Section: Image Fingerprinting In Crowd-pan-360mentioning

confidence: 97%

“…Another property of image perceptual hashing is discrimination which implies that the hash values for any two distinct images should be different and random. Due to the robustness and discrimination properties, we have chosen the perceptual hash algorithm in [18] to generate fingerprints for all the images in our dataset. This gives flexibility in image matching, since, the crowd-sourced images of the same place can be of different quality and texture depending on the camera quality and the time of the day when the image was taken.…”

Section: Image Fingerprinting In Crowd-pan-360mentioning

confidence: 99%

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CROWD-PAN-360: Crowdsourcing Based Context-Aware Panoramic Map Generation for Smartphone Users

Raychoudhury

Shrivastav²,

Sandha

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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Recent advances in smartphones and location-aware services necessitate identifying logical locations of users, in terms of their surroundings, instead of raw location coordinates. In this paper, we have proposed CROWD-PAN-360 (CP360), a novel smartphone-based system to generate 360-degree panoramic map of a querying user for his unfamiliar surrounding using crowd-sourced images. The objects (logical locations) appearing in the images are identified using manually or automatically generated tags. The system is context-aware and it intelligently associates user location coordinates with several smartphone contexts, like acceleration and orientation. CP360 can significantly reduce GPS positional errors for even cheap low-end smartphones and can identify the user surroundings very efficiently. We extensively tested the system in both indoor and outdoor environments of IIT Roorkee campus using Android smartphones over a dataset of more than 6000 crowd-sourced images of nearly 70 objects (departments, hostels, cafeteria, etc.) and CP360 generates the panoramic map with an average accuracy of 92.2%.

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Section: Image Fingerprinting In Crowd-pan-360mentioning

confidence: 97%

Section: Image Fingerprinting In Crowd-pan-360mentioning

confidence: 99%

CROWD-PAN-360: Crowdsourcing Based Context-Aware Panoramic Map Generation for Smartphone Users

Raychoudhury

Shrivastav²,

Sandha

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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show abstract

“…Image Perceptual Hashing [18] is mainly used in computing the similarity of images. Perceptual Hashing converts the image into binary mode.…”

Section: Image Perceptual Hashingmentioning

confidence: 99%

PH-CNN for PE Malware Detection Using Enhanced Image

2022

Preprint

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In the current malware family classification, gray images converted frombinary PE files are subject to code shelling, obfuscation, and other variant techniques, which undermine the similarity of malware images between same family. To solve this problem and improve detection efficiency, a method by enhancing the gray image and using PH-CNN as the detection model is proposed. First, the original image is enhanced by discarding the subjectively set machine code (DSMD) and adding section distribution information (ASDI), and then the enhanced imageis detected by PH-CNN model. Among them, the PH-CNN model consists of Perceptual Hashing detection module and CNN detection module. Malware images are first detected by Perceptual Hashing module to quickly divide the samples of specific malware families and uncertain malware families, and then, the samples of uncertain malware families are detected by the CNN detection module. The experimental results on Microsoft Malware Classification Challenge dataset show that compared with the original gray image, the detection accuracy using enhanced image is higher, reaching 98.68%. Meanwhile, compared with using only CNN module, the proposed detection model has higher detection efficiency and the detection speed is improved by 79%.

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Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos)

Dong

et al. 2022

Gastrointestinal Endoscopy

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A Robust and Discriminative Image Perceptual Hash Algorithm

Cited by 14 publications

References 9 publications

CROWD-PAN-360: Crowdsourcing Based Context-Aware Panoramic Map Generation for Smartphone Users

CROWD-PAN-360: Crowdsourcing Based Context-Aware Panoramic Map Generation for Smartphone Users

PH-CNN for PE Malware Detection Using Enhanced Image

Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos)

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