A Survey on Image Hashing for Image Authentication

Ou, Yang; Rhee, Kyung Hyune

doi:10.1587/transinf.e93.d.1020

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…We also applied computationally simple average hashing to images, despite the availability of more advanced methods for image fingerprinting (e.g. Ou and Rhee, 2010) and crowdsourcing approaches. This simple approach might have limited the findings but was appropriate for an initial investigation of large-scale image circulation using visual big data analysis methods.…”

Section: Methodsmentioning

confidence: 99%

Affective economy of national-populist images: Investigating national and transnational online networks through visual big data

Hokka

Nelimarkka

2019

New Media & Society

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In our article, we investigate the affective economy of national-populist image circulation on Facebook. This is highly relevant, since social media has been an essential area for the spread of national-populist ideology. In our research, we analyse image circulation as affective practice, combining qualitative and quantitative methods. We use computational data analysis methods to examine visual big data: image fingerprints and reverse image search engines to track down the routes of thousands of circulated images as well as make discourse-historical analysis on the images that have gained most attention among supporters. Our research demonstrates that these existing tools allow social science research to make theory-solid approaches to understand the role of image circulation in creating and sustaining national and transnational networks on social media, and show how national-populist thinking is spread through images that catalyse and mobilise affects -fear, anger and resentment -thus creating an effective affective economy.

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Section: Methodsmentioning

confidence: 99%

Affective economy of national-populist images: Investigating national and transnational online networks through visual big data

Hokka

Nelimarkka

2019

New Media & Society

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“…Embedding a stego message will break the hash and therefore prevent copies to be found. This can be solved by robust or perceptual hashes like discussed in [14] or [16]. With these hashes, multiple copies of images which differ regarding their binary representation, but look identical to a human observer, can be found reliably and efficiently.…”

Section: Research Goalmentioning

confidence: 99%

Cover-aware Steganalysis

Bunzel

Steinebach

Liu

2021

JCSANDM

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The increasing digitization offers new ways, possibilities and needs for a secure transmission of information. Steganography and its analysis constitute an essential part of IT-Security. In this work we show how methods of blind-steganalysis can be improved to work in cover-aware scenarios, we will call this non-blind steganalysis. The main objective was to examine how to take advantage of the knowledge of reference images to maximize the accuracy-rate of the analysis. Therefore we evaluated common stego-tools and their embedding algorithms and established a dataset of 353110 images. The images have been applied to test the potency of the improved methods of the non-blind steganalysis. The results show that the accuray can be significantly improved by using cover-images to produce reference images. Also the aggregation of the outcomes has shown to have a positive impact on the accuracy. Particularly noteworthy is the correlation between the qualities of the stego- and coverimages. Only by consindering both, the accuracy could strongly be improved. Interestingly the difference between both qualities also has a deep impact on the results.

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