Photo-sharing Research Articles

RETRACTED ARTICLE: Secret image sharing scheme with encrypted shadow images using optimal homomorphic encryption technique

Secret Image Sharing (SIS) scheme is to encrypt a secret image into ‘n’ specious shadows. It is unable to reveal any data on the secret image if at least one of the shadows is not achieved. In this paper, wavelet-based secret image sharing scheme is proposed with encrypted shadow images using optimal Homomorphic Encryption (HE) technique. Initially, Discrete Wavelet Transform (DWT) is applied on the secret image to produce sub bands. From this process, multiple shadows are created, encrypted and decrypted for each shadow. The encrypted shadow can be recovered just by choosing some subset of these ‘n’ shadows that makes transparent and stack over each other. To improve the shadow security, each shadow is encrypted and decrypted using HE technique. For the concern on image quality, the new Oppositional based Harmony Search (OHS) algorithm was utilized to generate the optimal key. From the analysis, it shows that the proposed scheme provide greater security compared to other existing schemes.

Going on a Field Trip: Critical Geographical Walking Tours and Tactical Media as Urban Praxis in Sydney, Australia

Going on a Field Trip: Critical Geographical Walking Tours and Tactical Media as Urban Praxis in Sydney, Australia

XOR Based Secret Image Sharing Scheme with Security Improvement *

XOR Based Secret Image Sharing Scheme with Security Improvement *

Chinese Remainder Theorem-Based Secret Image Sharing with Small-Sized Shadow Images

Secret image sharing (SIS) with small-sized shadow images has many benefits, such as saving storage space, improving transmission time, and achieving information hiding. When adjacent pixel values in an image are similar to each other, the secret image will be leaked when all random factors of an SIS scheme are utilized for achieving small sizes of shadow images. Most of the studies in this area suffer from an inevitable problem: auxiliary encryption is crucial in ensuring the security of those schemes. In this paper, an SIS scheme with small-sized shadow images based on the Chinese remainder theorem (CRT) is proposed. The size of shadow images can be reduced to nearly 1 / k of the original secret image. By adding random bits to binary representations of the random factors in the CRT, auxiliary encryption is not necessary for this scheme. Additionally, reasonable modifications of the random factors make it possible to incorporate all advantages of the CRT as well, including a ( k , n ) threshold, lossless recovery, and low computation complexity. Analyses and experiments are provided to demonstrate the effectiveness of the proposed scheme.

The presence of tourists and residents in shared travel photos

This study aims to reveal the extent of human presence in travel photos shared in the social media by tourists who traveled to Iran. We unearthed the host-guest interaction that is distinctive in those photos. To do so, we investigated 812 random photos shared by 186 tourists on Facebook. We employed quantitative and qualitative content analysis to categorize and define photos. We found that people are absent in sixty percent of shared photos. The biggest share of photos with the presence of humans comprise those featuring tourists and their travel companions only, and in which portrait photos have the biggest share. About one third of photos with human presence include residents only, and the western-dominance and reporting approach is noticeable. Only in ten percent of all photos can tourists and hosts be seen beside each other, and we categorize these according to three levels of interaction.

THE INSTAGRAM EFFECT: EXPLORING CONSUMERS’ SHOPPING BEHAVIOR AND ITS IMPACT ON PURCHASE INTENTION

Consumers have enjoyed shopping via the web for over a decade, but the emergence of shopping through social media or social commerce is slowly gaining traction Instagram is one of the largest interactive photo-sharing sites that retailers, specifically apparel and accessory companies, are utilizing to sell their merchandise. Consumers are increasingly using this platform to engage, discover and get inspired; therefore, it is critical for retailers to understand how this platform influences purchase intention. This paper focuses on analyzing the impact of Instagram on consumer’s purchase intention based on an adapted model of Technology Acceptance Model and Theory of Reasoned Action. Using multi-group analysis, consumers (n=317) were divided into two groups: shopped for fashion products (including browsing, making a one-time purchase, or making repeat purchases) or not shopped using Instagram for apparel specific brands. The participants were surveyed using an online instrument with questions related to attitude, subjective norms, normative beliefs, perceived usefulness (PU), perceived ease of use (PEOU) and purchase intention. Frequency statistics were obtained for the demographic variables. Overwhelmingly (approximately 78%), respondents used Instagram daily with 82.6% following an apparel brand. 40% of the respondents had shopped previously on Instagram (i.e. browsed as well as purchased products), with 13% of these respondents spending more than $100. An exploratory factor analysis using principal component with varimax rotation and a minimum eigen value of one was used to identify the latent variables in the model: PEOU (α = 0.82); PU (α = 0.81); Normative Beliefs (α = 0.81); Attitude (α = .87); Subjective Norm (α = 0.88); Purchase Intention (α = 0.79). Results indicated that subjective norms did not influence purchase intention for both the groups indicating that consumers do not follow “groupthink” mentality while intending to purchase via Instagram. Furthermore, it was found that PEOU influence on attitude for the two groups of consumer were different, with no impact for the group who had shopped on Instagram. All the other relationships were supported in the model. These results provide both implications and limitations for retailers and academia.

Crowdsourcing architectural beauty: Online photo frequency predicts building aesthetic ratings.

The aesthetic quality of the built environment is of paramount importance to the quality of life of an increasingly urbanizing population. However, a lack of data has hindered the development of comprehensive measures of perceived architectural beauty. In this paper, we demonstrate that the local frequency of geotagged photos posted by internet users in two photo-sharing websites strongly predict the beauty ratings of buildings. We conduct an independent beauty survey with respondents rating proprietary stock photos of 1,000 buildings across the United States. Buildings with higher ratings were found more likely to be geotagged with user-uploaded photos in both Google Maps and Flickr. This correlation also holds for the beauty rankings of raters who seldom upload materials to the internet. Objective architectural characteristics that predict higher average beauty ratings of buildings also positively covary with their internet photo frequency. These results validate the use of localized user-generated image uploads in photo-sharing sites to measure the aesthetic appeal of the urban environment in the study of architecture, real estate, urbanism, planning, and environmental psychology.

Hierarchical information quadtree: efficient spatial temporal image search for multimedia stream

Massive amount of multimedia data that contain times- tamps and geographical information are being generated at an unprecedented scale in many emerging applications such as photo sharing web site and social networks applications. Due to their importance, a large body of work has focused on efficiently computing various spatial image queries. In this paper,we study the spatial temporal image query which considers three important constraints during the search including time recency, spatial proximity and visual relevance. A novel index structure, namely Hierarchical Information Quadtree(\hiq), to efficiently insert/delete spatial temporal images with high arrive rates. Base on \hiq an efficient algorithm is developed to support spatial temporal image query. We show via extensive experimentation with real spatial databases clearly demonstrate the efficiency of our methods.

An Improved Essential Secret Image Sharing Scheme with Smaller Shadow Size

This article describes how in conventional (k, n) secret image sharing (SIS), any k or more shadows can be collaborated together to reveal the secret image. Usually, all shadows have the same importance. Recently, an essential (t, s, k, n) secret image sharing (ESIS) with different importance of shadows were proposed where the n shadows are divided into s essential shadows and n − s non-essential shadows. In reconstruction, it needs at least k shadows which should include at least t essential shadows. In this article, the authors propose an improved (t, s, k, n)-ESIS scheme, where t<k. In their scheme, essential shadows and non-essential shadows have the equal sizes. No concatenation operation is needed in generating shadows. Besides, all shadow sizes are reduced to 1/k times of the secret image. Theoretical analyses and experimental results show that the proposed scheme is feasible with optimal sharing ratios among related works.

Lossless and Efficient Polynomial-Based Secret Image Sharing with Reduced Shadow Size

Thien-and-Lin’s polynomial-based secret image sharing (PSIS) is utilized as the basic method to achieve PSISs with better performances, such as meaningful shares, two-in-one property and shares with different priorities. However, this (k,n) threshold PSIS cannot achieve lossless recovery for pixel values more than 250. Furthermore, current solutions to lossless recovery for PSIS have several natural drawbacks, such as large computational costs and random pixel expansion. In this paper, a lossless and efficient (k,n) threshold PSIS scheme with reduced shadow size is presented. For lossless recovery and efficiency, two adjacent pixels are specified as a secret value, the prime in the sharing polynomial is replaced with 65,537, and then the additional screening operation can ensure each shared value in the range [0,65,535]. To reduce shadows size and improve security, only the first k−1 coefficients are embedded with secret values and the last coefficient is assigned randomly. To prevent the leakage of secrets, generalized Arnold permutation with special key generating strategy is performed on the secret image prior to sharing process without key distribution. Both theoretical analyses and experiments are conducted to demonstrate the effectiveness of the proposed scheme.

A Verifiable Secret Image Sharing Scheme Based on Compressive Sensing

This paper proposes a verifiable secret image sharing scheme based on compressive sensing, secret sharing, and image hashing. In this scheme, Toeplitz matrix generated by two chaotic maps is employed as measurement matrix. With the help of Shamir threshold scheme and image hashing, the receivers can obtain the stored values and the hash value of image. In the verifying stage and restoring stage, there must be at least t legal receivers to get the effective information. By comparing the hash value of the restored image with the hash value of original image, the scheme can effectively prevent the attacker from tampering or forging the shared images. Experimental results show that the proposed scheme has good recovery performance, can effectively reduce space, and is suitable for real-time transmission, storage, and verification.

Leveraging Content Sensitiveness and User Trustworthiness to Recommend Fine-Grained Privacy Settings for Social Image Sharing

To configure successful privacy settings for social image sharing, two issues are inseparable: 1) content sensitiveness of the images being shared; and 2) trustworthiness of the users being granted to see the images. This paper aims to consider these two inseparable issues simultaneously to recommend fine-grained privacy settings for social image sharing. For achieving more compact representation of image content sensitiveness (privacy), two approaches are developed: 1) a deep network is adapted to extract 1024-D discriminative deep features; and 2) a deep multiple instance learning algorithm is adopted to identify 280 privacy-sensitive object classes and events. Second, users on the social network are clustered into a set of representative social groups to generate a discriminative dictionary for user trustworthiness characterization. Finally, both the image content sensitiveness and the user trustworthiness are integrated to train a tree classifier to recommend fine-grained privacy settings for social image sharing. Our experimental studies have demonstrated both the efficiency and the effectiveness of our proposed algorithms.

Identifying Private Content for Online Image Sharing

I present the outline of my dissertation work, Identifying Private Content for Online Image Sharing. Particularly, in my dissertation, I explore learning models to predict appropriate binary privacy settings (i.e., private, public) for images, before they are shared online. Specifically, I investigate textual features (user-annotated tags and automatically derived tags), and visual semantic features that are transferred from various layers of deep Convolutional Neural Network (CNN). Experimental results show that the learning models based on the proposed features outperform strong baseline models for this task on the Flickr dataset of thousands of images.

The New Urban Success: How Culture Pays

Urban economists have put forward the idea that cities that are culturally interesting tend to attract "the creative class" and, as a result, end up being economically successful. Yet it is still unclear how economic and cultural dynamics mutually influence each other. By contrast, that has been extensively studied in the case of individuals. Over decades, the French sociologist Pierre Bourdieu showed that people's success and their positions in society mainly depend on how much they can spend (their economic capital) and what their interests are (their cultural capital). For the first time, we adapt Bourdieu's framework to the city context. We operationalize a neighborhood's cultural capital in terms of the cultural interests that pictures geo-referenced in the neighborhood tend to express. This is made possible by the mining of what users of the photo-sharing site of Flickr have posted in the cities of London and New York over 5 years. In so doing, we are able to show that economic capital alone does not explain urban development. The combination of cultural capital and economic capital, instead, is more indicative of neighborhood growth in terms of house prices and improvements of socio-economic conditions. Culture pays, but only up to a point as it comes with one of the most vexing urban challenges: that of gentrification.

Polynomial-Based Secret Image Sharing Scheme with Fully Lossless Recovery

Lossless recovery is important for the transmission and storage of image data. In polynomial-based secret image sharing, despite many previous researchers attempted to achieve lossless recovery, none of the proposed work can simultaneously satisfy an efficiency execution and at no cost of some storage capacity. This article proposes a secret sharing scheme with fully lossless recovery based on polynomial-based scheme and modular algebraic recovery. The major difference between the proposed method and polynomial-based scheme is that, instead of only using the first coefficient of sharing polynomial, this article uses the first two coefficients of sharing polynomial to embed the pixels as well as guarantee security. Both theoretical proof and experimental results are given to demonstrate the effectiveness of the proposed scheme.

A cellular automata-based multi-stage secret image sharing scheme

A new Multi-Stage Multi-Secret Image Sharing (MSMSIS) scheme, based on polynomial sharing and cellular automata, is proposed in this paper. The proposed scheme shares multiple secret images among a number of players. The shares and the authentication strings, produced by a cryptographic hash function, are then embedded in cover images by an XOR-based operation in such a way that visual and statistical quality of covers are preserved as much as possible. A particular type of cellular automata is used to guarantee that the secrets are only recovered according to a pre-specified order. The scheme is multi-use, since some parameters are distributed only once and used multiple times. The scheme is verifiable as well, because a cryptographic hash function is used in each stage for each secret image separately. Generality of our scheme also implies that it can be applied on a heterogeneous set of different secret formats, where each secret has its own access structure. Based on our experiments, the PSNR of the proposed method is improved almost 11 percent compared to the average PSNR of the previous similar schemes.

Nuevos datos sobre la distribución de <em>Lipara</em> Meigen, 1830 (Diptera: Chloropidae) y sus clorópidos inquilinos en la península ibérica

El género Lipara Meigen, 1830 (Diptera: Chloropidae) tiene la capacidad de inducir agallas en Phragmites australis (Cav.) Trin. ex Steud., 1841 (Poaceae). Dichas agallas son microhábitats importantes que aportan recursos a otros clorópidos que viven dentro de ellas. En la península ibérica, las agallas de Lipara y la fauna asociada no han sido bien estudiadas. En este trabajo se recogen nuevas citas de Lipara lucens Meigen, 1830, L. similis Schiner, 1854 y sus inquilinos, Calamoncosis minima (Strobl, 1893) y Cryptonevra flavitarsis (Meigen, 1830) en la península ibérica, combinando muestreos en campo, con información disponible en portales fotográficos. Se citan por primera vez en la España peninsular L. lucens, C. minima y Cr. flavitarsis. A pesar de que son un grupo que ha sido poco estudiado, estas especies parecen estar ampliamente distribuidas por la península ibérica.

Detecting tourist attractions using geo-tagged photo clustering

Millions of geo-tagged photos are becoming available due to the wide spread of photo-sharing websites, which provide valuable information to mine spatial patterns from human activities. In this study, we present a simple and fast density-based spatial clustering algorithm to detect popular scenic spots using geo-tagged photos collected from Flickr. In this algorithm, Gaussian kernel is applied to estimate local density of data points, and a decision graph is used to obtain cluster centers easily. More than 289,000 geo-tagged photos located in five typical cities of China are downloaded as case studies, and data pre-processing such as duplicate removing is performed to improve the quality of clustering result. Finally, popular tourist attractions of each sample city are successfully detected with this algorithm, and our result is useful for recommending some interesting destinations which might not be on the list of tourist website or mobile guide applications. The proposed solution is robust with respect to different distributions of photos, and it is efficient by comparing with other popular clustering approaches.

Determining preferences for ecosystem benefits in Great Lakes Areas of Concern from photographs posted to social media.

Relative valuation of potentially affected ecosystem benefits can increase the legitimacy and social acceptance of ecosystem restoration projects. As an alternative or supplement to traditional methods of deriving beneficiary preference, we downloaded from social media and classified ≈21,000 photographs taken in two Great Lakes Areas of Concern (AOC), the St. Louis River and the Milwaukee Estuary. Our motivating presumption was that the act of taking a photograph constitutes some measure of the photographer's individual preference for, or choice of, the depicted subject matter among myriad possible subject matter. Overall, 17% of photos downloaded from the photo-sharing sites Flickr, Instagram, and Panoramio depicted an ecosystem benefit of the AOC. Percent of photographs depicting a benefit and the photographs' subject matter varied between AOCs and among photo-sharing sites. Photos shared on Instagram were less user-gender biased than other photo-sharing sites and depicted active recreation (e.g., trail use) more frequently than passive recreation (e.g., landscape viewing). Local users shared more photos depicting a benefit than non-local users. The spatial distribution of photograph locations varied between photos depicting and not depicting a benefit, and identified areas within AOCs from which few photographs were shared. As a source of beneficiary preference information, we think Instagram has some advantages over the other photo-sharing sites. When combined with other information, spatially-explicit relative valuation derived from aggregate social preference can be translated into information and knowledge useful for Great Lakes restoration decision making.

Dermatology on Flickr

Flickr, which was launched in 2004, is one of the oldest photo-sharing services online. The company, which was once at the forefront of the internet social media explosion, lost users and received criticism as its repeated acquisitions by other companies forced users to adapt to new owners. Now facing another acquisition by the online photo-sharing service SmugMug, the future is uncertain again for the once popular internet and mobile application service. Flickr, with its rapid rise in popularity, sudden halt in growth, and its gradual loss of users and popularity, has been repeatedly looked to as a lesson for other internet startups on the necessity of adapting quickly in the current internet age. Given the rise of social media use in the healthcare field, and the long-standing presence of Flickr on the internet, this study investigated the presence of dermatology-related content on Flickr and how the photo-sharing platform has applied to the visual field of dermatology, its dermatologists, and its professional associations.