Manoranjan is a lecturer in the Center for Forensic Science at the School of Mathematical and Physical Science. He comes from Computer Science background. His research interest is on digital forensics and cybersecurity, with current focus mainly on source camera attribution, child explicit content detection, fake food detection, privacy-aware forensics, cloud and IoT forensics, and application of deep learning and blockchain for forensics. Manoranjan received his Ph.D. in Computer Science from the National University of Singapore, Singapore in 2014. After that, he spent a year as an ERCIM Alain Bensoussan research fellow at SICS Swedish ICT, Sweden, and two years as a research fellow at New York University. Before joining UTS, he was a Lecturer in Digital Security at the University of Auckland, New Zealand.
- Event leader (Digital Forensics) of 2017 Cyber Security Awareness Week and 2016 Cyber Security Awareness Week
- Organizer of the 2017 IEEE SPS Winter School on Security and Privacy Issues in Biometrics
- Represented more than 500 graduate students to the university administration
- Received Certificate of Achievements for the leadership
- Listed as Quality Reviewer by 2013 IEEE ICME conference
- Listed as Exemplary Reviewer by ETRI Journal
- Publicity Co-Chair of IEEE BIGMM 2019 conference
- Program Chair of ICIT 2019 conference
- Editorial Board Member of Current Trends In Computer Sciences and Applications
- Organizer of one special session in IEEE SMC 2019 conference
- Organizer of two special sessions in IEEE CEC 2019 conference
- Associate Editor of International Journal of Digital Crime and Forensics
- Web Chair of ACM ASIACCS 2019
- Publication Chair of IEEE AVSS 2018 conference
- Publicity Chair of IEEE SSCI 2018 conference
- Session Chair at IEEE AVSS 2018, SECRYPT 2017, SECRYPT 2016, MMM 2015
- Member of the Technical Program Committee for ICSM 2018, PIM 2018, FakeMM 2018, PIM 2016, MMM 2015, MMEDIA 2015, and MMEDIA 2014 conferences
Can supervise: YES
- Digital Forensics
- Applied Cryptography
- Privacy Preserving Techniques
- Cloud-Based Secure Multimedia
- Blockchain for Healthcare
- IoT Security and Forensics
- Cloud, Edge, Fog Security
- Digital Forensics
- Cyber Security
- Network Security
- Systems Security
Bharill, N, Patel, OP, Tiwari, A, Mu, L, Li, D-L, Mohanty, M, Kaiwartya, O & Prasad, M 2019, 'A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering', IEEE ACCESS, vol. 7, pp. 50347-50361.View/Download from: UTS OPUS or Publisher's site
Mohanty, M, Zhang, M, Asghar, MR & Russello, G 2019, 'e-PRNU: Encrypted Domain PRNU-Based Camera Attribution for Preserving Privacy', IEEE Transactions on Dependable and Secure Computing.View/Download from: Publisher's site
IEEE Photo Response Non-Uniformity (PRNU) noise-based source camera attribution is a popular digital forensic method. In this method, a camera fingerprint computed from a set of known images of the camera is matched against the extracted noise of an anonymous questionable image to find out if the camera had taken the anonymous image. The possibility of privacy leak, however, is one of the main concerns of the PRNU-based method. Using the camera fingerprint (or the extracted noise), an adversary can identify the owner of the camera by matching the fingerprint with the noise of an image (or with the fingerprint computed from a set of images) crawled from a social media account. In this article, we address this privacy concern by encrypting both the fingerprint and the noise using the Boneh-Goh-Nissim (BGN) encryption scheme, and performing the matching in encrypted domain. To overcome leakage of privacy from the content of an image that is used in the fingerprint calculation, we compute the fingerprint within a trusted environment, such as ARM TrustZone. We present e-PRNU that aims at minimizing privacy loss and allows authorized forensic experts to perform camera attribution. The security analysis shows that the proposed approach is semantically secure.
El-Sayed, H, Sankar, S, Daraghmi, Y-A, Tiwari, P, Rattagan, E, Mohanty, M, Puthal, D & Prasad, M 2018, 'Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier.', Sensors (Basel, Switzerland), vol. 18, no. 6.View/Download from: UTS OPUS or Publisher's site
Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.
El-Sayed, H, Sankar, S, Prasad, M, Puthal, D, Gupta, A, Mohanty, M & Lin, CT 2018, 'Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment', IEEE Access, vol. 6, pp. 1706-1717.View/Download from: UTS OPUS or Publisher's site
© 2013 IEEE. A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network de vices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.
Taspinar, S, Mohanty, M & Memon, N 2017, 'PRNU-Based Camera Attribution from Multiple Seam-Carved Images', IEEE Transactions on Information Forensics and Security, vol. 12, no. 12, pp. 3065-3080.View/Download from: Publisher's site
© 2017 IEEE. Photo response non-uniformity (PRNU) noise-based source attribution is a well-known technique to verify the camera of an image or video. Researchers have proposed various countermeasures to prevent PRNU-based source camera attribution. Forced seam-carving is one such recently proposed counter forensics technique. This technique can disable PRNU-based source camera attribution by forcefully removing seams such that the size of most uncarved image blocks is less than $50 \times 50$ pixels. In this paper, we show that given multiple seam-carved images from the same camera, source attribution can still be possible even if the size of uncarved blocks in the image is less than the recommended size of $50 \times 50$ pixels. Theoretical analysis and experiments with multiple cameras demonstrate that the effectiveness of our scheme depends on the number of seams carved from an image and the randomness of the seam positions.
Mohanty, M, Asghar, MR & Russello, G 2016, '2DCrypt: Image Scaling and Cropping in Encrypted Domains', IEEE Transactions on Information Forensics and Security, vol. 11, no. 11, pp. 2542-2555.View/Download from: Publisher's site
© 2016 IEEE. The evolution of cloud computing and a drastic increase in image size are making the outsourcing of image storage and processing an attractive business model. Although this outsourcing has many advantages, ensuring data confidentiality in the cloud is one of the main concerns. There are state-of-the-art encryption schemes for ensuring confidentiality in the cloud. However, such schemes do not allow cloud datacenters to perform operations over encrypted images. In this paper, we address this concern by proposing 2DCrypt, a modified Paillier cryptosystem-based image scaling and cropping scheme for multi-user settings that allows cloud datacenters to scale and crop an image in the encrypted domain. To anticipate a high storage overhead resulted from the naive per-pixel encryption, we propose a space-efficient tiling scheme that allows tile-level image scaling and cropping operations. Basically, instead of encrypting each pixel individually, we are able to encrypt a tile of pixels. 2DCrypt is such that multiple users can view or process the images without sharing any encryption keys - a requirement desirable for practical deployments in real organizations. Our analysis and results show that 2DCrypt is INDistinguishable under Chosen Plaintext Attack secure and incurs an acceptable overhead. When scaling a 512 × 512 image by a factor of two, 2DCrypt requires an image user to download approximately 5.3 times more data than the un-encrypted scaling and need to work approximately 2.3 s more for obtaining the scaled image in a plaintext.
Mohanty, M, Ooi, WT & Atrey, PK 2016, 'Secret sharing approach for securing cloud-based pre-classification volume ray-casting', Multimedia Tools and Applications, vol. 75, no. 11, pp. 6207-6235.View/Download from: Publisher's site
© 2015, Springer Science+Business Media New York. With the evolution in cloud computing, cloud-based volume rendering, which outsources data rendering tasks to cloud datacenters, is attracting interest. Although this new rendering technique has many advantages, allowing third-party access to potentially sensitive volume data raises security and privacy concerns. In this paper, we address these concerns for cloud-based pre-classification volume ray-casting by using Shamir’s (k, n) secret sharing and its variant (l, k, n) ramp secret sharing, which are homomorphic to addition and scalar multiplication operations, to hide color information of volume data/images in datacenters. To address the incompatibility issue of the modular prime operation used in secret sharing technique with the floating point operations of ray-casting, we consider excluding modular prime operation from secret sharing or converting the floating number operations of ray-casting to fixed point operations – the earlier technique degrades security and the later degrades image quality. Both these techniques, however, result in significant data overhead. To lessen the overhead at the cost of high security, we propose a modified ramp secret sharing scheme that uses the three color components in one secret sharing polynomial and replaces the shares in floating point with smaller integers.
Mohanty, M 2012, 'New renewable energy sources, green energy development and climate change: Implications to Pacific Island countries', Management of Environmental Quality, vol. 23, no. 3, pp. 264-274.View/Download from: Publisher's site
Purpose: The aim of the paper is to examine the renewable energy resources for enhancing a green energy development in the face of energy crisis and climate change, and to explore the prospects for "new" renewable energy sources and the green energy initiatives taken in the Pacific Island countries (PICs). Design/methodology/approach: The data were collated from a wide variety of sources including policy documents, road maps, reports, research articles on renewable and green energy sources. The methodology adopted was primarily a qualitative one based on a "content analysis". Findings: The findings reveal that increasing emphases have been given recently to "new" renewable and green energy sources in the Pacific Island countries as mitigation and adaptation strategies to fuel crisis and climate change. PICs have taken a wide range of green energy initiatives including "biomass", solar, wind and other non-traditional renewable energy sources and bio-fuels development. Prospects for coconut, copra and palm-oil based bio-fuels do exist in many PICs. Opportunities for ethanol bio-fuels also exist especially in Fiji. Practical implications: Renewable and green energy sources are of practical implications to PICs. There is, however, a greater need for framing sound energy policies by the PICs. Originality/value: The author has brought out clear linkages between climate change and green energy development and analyzed the importance of new renewable energy sources, especially in PICs. The paper has higher policy relevance and it is of great value in the context of sustainable energy development in PICs. © Emerald Group Publishing Limited.
Reddy, M, Mohanty, M & Naidu, V 2004, 'Economic cost of human capital loss from Fiji: Implications for sustainable development', International Migration Review, vol. 38, no. 4, pp. 1447-1461.
Small island nations in the South Pacific are facing a serious problem of loss of human capital. The loss of skilled and qualified personnel from the small pool is causing a major setback in terms of providing the technical expertise to forge ahead with reform programs that these economies are undertaking. Fiji's policymakers are increasingly confronting this issue, because the nation has experienced a massive outflow of skilled labor following the political instability in 1987. While there is an outflow of skilled labor, the country is also losing a large amount of financial capital. The extent of the outflow has yet to be measured due to lack of a methodology. This study advances a methodology to measure the loss to the economy arising out of human capital loss in a small island economy. © 2004 by the Center for Migration Studies of New York. All rights reserved.
McDonald, L, Naidu, V & Mohanty, M 2014, 'Vulnerability, resilience and dynamism of the custom economy in Melanesia' in Household Vulnerability and Resilience to Economic Shocks: Findings from Melanesia, pp. 107-127.
Mohanty, M, Zhang, M & Russello, G 2019, 'A Photo Forensics-Based Prototype to Combat Revenge Porn', Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019, 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), IEEE, San Jose, CA, USA, pp. 5-8.View/Download from: UTS OPUS or Publisher's site
© 2019 IEEE. The proliferation of image-and video-sharing websites has increased revenge porn images. In this paper, we propose a photo forensic-based prototype to aid revenge porn victims to automatically retrieve offending images. Our prototype combines a deep learning-based nude detection filter with a face recognition filter to find if a nude image containing the victim's face is posted on targeted social media accounts. After finding revenge porn images from the targeted accounts, our prototype iteratively finds other accounts containing potential revenge porn images. Revenge porn images from these newly identified accounts are then filtered out. Experiment with 1254 images shows that our method can filter revenge porn images in 91% cases.
Naidu, V & Mohanty, M 2019, 'Defeating fake food labels using watermarking and biosequence analysis', Proceedings - 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019, pp. 435-441.View/Download from: Publisher's site
© 2019 IEEE. Fake food label is one of the leading ways to distribute a low quality food item as a high quality branded product. For example, under fake labels, significantly higher amount of fake Manuka honey is sold than what is actually being produced. In this paper, we propose a scheme to combat the spread of such low quality food items by identifying fake food labels. In our scheme, a watermarking is inserted to a genuine food label and biosequence analysis is used to detect this watermark. The proposed biosequence analysis is such that it can detect duplicate labels, for example a photocopy of the genuine label. The proposed method works by converting a label image into biological amino acid form (e.g., to A, C, D, G, H, etc. form) and then extracting a signature from the label (which is represented in amino acid form) using biological tools. These signatures are then matched against a query label image to find out its originality. Experiment with honey food labels (honey watermarked dataset created by us) shows that the proposed method has true positive rate of 91:67%.
Naidu, V, Narayanan, A & Mohanty, M 2019, 'Using Amino Acids of Images for Identifying Pornographic Images', Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019, 2019 IEEE Conference on Multimedia Information Processing and Retrieval, San Jose, CA, USA, pp. 9-14.View/Download from: UTS OPUS or Publisher's site
© 2019 IEEE. The pornographic images need to be regulated as they can have an adverse effect on the society. This paper purposes an image amino acid-based method to identify a pornographic image. The proposed method works by converting an image into biological amino acid form (e.g., to A, C, D, G, H, etc. form) and then extracting a signature from the image (which is represented in amino acid form) using biological tools. Using this method, a number of signatures are obtained from a set of few known pornographic images. These signatures are then matched against a database of images to find out the pornographic images. The matching is done using the openly available anti-virus scanner Clamscan. Here, the signatures obtained from the pornographic images are represented as signatures of viruses. The experimental result shows that the proposed method can identify a pornographic image with a high detection rate.
Shah, MD, Mohanty, M & Atrey, PK 2019, 'SecureCSearch: Secure Searching in PDF over Untrusted Cloud Servers', Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019, pp. 347-352.View/Download from: Publisher's site
© 2019 IEEE. The usage of cloud for data storage has become ubiquitous. To prevent data leakage and hacks, it is common to encrypt the data (e.g. PDF files) before sending it to a cloud. However, this limits the search for specific files containing certain keywords over an encrypted cloud data. The traditional method is to take down all files from a cloud, store them locally, decrypt and then search over them, defeating the purpose of using a cloud. In this paper, we propose a method, called SecureCSearch, to perform keyword search operations on the encrypted PDF files over cloud in an efficient manner. The proposed method makes use of Shamir's Secret Sharing scheme in a novel way to create encrypted shares of the PDF file and the keyword to search. We show that the proposed method maintains the security of the data and incurs minimal computation cost.
Yaqub, W, Mohanty, M & Memon, N 2019, 'Encrypted Domain Skin Tone Detection for Pornographic Image Filtering', Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance.View/Download from: Publisher's site
© 2018 IEEE. The unavailability of a pornographic image database has been an impediment for automated detection of child pornographic images. Beyond data confidentiality and privacy issues, even mere possession of such images is illegal in many countries. In this paper, these issues are addressed for skin tone detection, which is an essential component for filtering pornographic images. A pornographic image is encrypted using order preserving encryption, randomization, and permutation. Skin pixels are detected from the encrypted image in the encrypted domain without revealing the image content. Experiments and analysis show that the proposed scheme has low overhead and no degradation in detection accuracy.
Mohanty, M, Zhang, M, Asghar, MR & Russello, G 2018, 'PANDORA: Preserving Privacy in PRNU-Based Source Camera Attribution', Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018, pp. 1202-1207.View/Download from: Publisher's site
© 2018 IEEE. Photo Response Non-Uniformity (PRNU) noise-based source camera attribution is a popular digital forensic method. In this method, a camera fingerprint computed from a set of known images of the camera is matched against the extracted noise of an anonymous questionable image to find if the camera had taken the anonymous image. The possibility of privacy leak, however, is one of the main concerns of the PRNU-based method. Using the camera fingerprint (or the extracted noise), an adversary can identify the owner of the camera by matching the fingerprint with the noise of an image (or with the fingerprint computed from a set of images) crawled from a social media account. In this paper, we address this privacy concern by encrypting both the fingerprint and the noise using the Boneh-Goh-Nissim (BGN) encryption scheme, and performing the matching in encrypted domain. To overcome leakage of privacy from the content of an image that is used in the fingerprint calculation, we compute the fingerprint within a trusted environment, such as ARM TrustZone. We present PANDORA that aims at minimizing privacy loss and allows authorized forensic experts to perform camera attribution.
Yaqub, W, Mohanty, M & Memon, N 2018, 'Towards camera identification from cropped query images', Proceedings - International Conference on Image Processing, ICIP, pp. 3798-3802.View/Download from: Publisher's site
© 2018 IEEE. PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation. However, these techniques assume that the query images have not been scaled or cropped. In practice this may often not be the case, especially when query images are taken from social media sites. This paper presents a simple scaling-based approach for source camera identification when the query image is of full resolution or if it is cropped from an unknown location (of the original image). The proposed approach can also be easily combined with other known approaches for PRNU matching of scaled images. Experiments using 250 cameras showed that the run time overhead can decrease by a factor 13 when the query is a cropped image.
Durmus, E, Mohanty, M, Taspinar, S, Uzun, E & Memon, N 2017, 'Image carving with missing headers and missing fragments', 2017 IEEE Workshop on Information Forensics and Security, WIFS 2017, pp. 1-6.View/Download from: Publisher's site
© 2017 IEEE. Although some remarkable advancements have been made in image carving, even in the presence of fragmentation, existing methods are not effective when parts (fragments) of an image are missing. This paper addresses this problem and proposes a PRNU (Photo Response Non-Uniformity)-based image carving method. The proposed technique assumes that the underlying camera fingerprint (camera sensor noise) is available prior to the carving process. Given a large number of image fragments, the camera fingerprint is used to find the position of fragments in a to-be-carved image. Using all known-position-fragments, the number of to-be-carved images is then found. The known-position-fragments and the unknown-position-fragments are placed on these images using two different greedy algorithms. Experiment with 23040 fragments shows that the proposed scheme has a true positive rate of 94.2%.
Fan, G & Mohanty, M 2017, 'Privacy-preserving disease susceptibility test with shamir's secret sharing', ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications, pp. 525-533.
Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Recent advances in genomics have facilitated the development of personalized medicine, in which a patient's susceptibility to certain diseases and her compatibility with certain medications can be determined from her genetic makeup. Although this technology has many advantages, privacy of the patient is one of the major concerns due to the sensitivity of genomic data. In this paper, we propose a privacy-preserving scheme for computing a patient's susceptibility to a particular disease. Our scheme stores genomic data in hidden form and performs the disease susceptibility test in the hidden domain. To hide the data, we use Shamir's (l, n) secret sharing, which can be homomorphic to a fixed number of multiplications and unlimited additions. Using Shamir's secret sharing, we create n shares and store the shares at n datacenters. The datacenters perform the susceptibility test on their shares and send the result (which is also hidden) to a hospital. Finally, the hospital obtains the secret result of the test by accessing at least k datacenters, where k = 2l-1. In comparison to other works, our approach is more practical as it minimizes the involvement of the patient and incurs less overhead.
Taspinar, S, Mohanty, M & Memon, N 2017, 'Source camera attribution using stabilized video', 8th IEEE International Workshop on Information Forensics and Security, WIFS 2016.View/Download from: Publisher's site
© 2016 IEEE. Although PRNU (Photo Response Non-Uniformity)-based methods have been proposed to verify the source camera of a non-stabilized video, these methods may not be adequate for stabilized videos. The use of video stabilization has been increasing in recent years with the development of novel stabilization software and the availability of stabilization in smart-phone cameras. This paper presents a PRNU-based source camera attribution method for out-of-camera stabilized video (i.e., stabilization applied after the video is captured). The scheme can (i) automatically determine if a given video is stabilized, (ii) calculate the fingerprint from a stabilized video, and (iii) effectively correlate the fingerprint computed from a stabilized video (i.e., anonymous video) with a fingerprint computed from another stabilized or non-stabilized video (i.e., a known video). Furthermore, experimental results show that the source camera of an anonymous non-stabilized video can be verified using a fingerprint computed from a set of images.
Mohanty, M, Asghar, MR & Russello, G 2016, '3DCrypt: Privacy-preserving pre-classification volume ray-casting of 3D images in the cloud', ICETE 2016 - Proceedings of the 13th International Joint Conference on e-Business and Telecommunications, pp. 283-291.View/Download from: Publisher's site
Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. With the evolution of cloud computing, organizations are outsourcing the storage and rendering of volume (i.e., 3D data) to cloud servers. Data confidentiality at the third-party cloud provider, however, is one of the main challenges. In this paper, we address this challenge by proposing - 3DCrypt - a modified Paillier cryptosystem scheme for multi-user settings that allows cloud datacenters to render the encrypted volume. The rendering technique we consider in this work is pre-classification volume ray-casting. 3DCrypt is such that multiple users can render volumes without sharing any encryption keys. 3DCrypt's storage and computational overheads are approximately 66.3 MB and 27 seconds, respectively when rendering is performed on a 256 x 256 x 256 volume for a 256 x 256 image space.
Taspinar, S, Mohanty, M & Memon, N 2016, 'PRNU based source attribution with a collection of seam-carved images', Proceedings - International Conference on Image Processing, ICIP, pp. 156-160.View/Download from: Publisher's site
© 2016 IEEE. Photo Response Non-Uniformity (PRNU) noise based source attribution is a well known technique to verify the source camera of an anonymous image or video. Researchers have proposed various counter measures to PRNU based source camera attribution. Forced seam-carving is a recently proposed counter forensics measure that was proposed to defeat PRNU based source attribution by disturbing the alignment of PRNU noise patterns. This paper shows that given a multiple number of seam-carved images, source attribution can still be reliably made even if the size of a non-carved image block is less than the recommended size of 50×50.
Kansal, K, Mohanty, M & Atrey, PK 2015, 'Scaling and cropping of wavelet-based compressed images in hidden domain', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 430-441.
© Springer International Publishing Switzerland 2015. With the rapid advancement of cloud computing, the use of third-party cloud datacenters for storing and processing (e.g, scaling and cropping) personal and critical images is becoming more common. For storage and bandwidth efficiency, the images are almost always compressed. Although cloud-based imaging has many advantages, security and privacy remain major issues. One way to address these two issues is to use Shamir’s (k, n) secret sharing-based secret image sharing schemes, which can distribute the secret image among n number of participants in such a way that no less than k (where k ≤ n) participants can know the image content. Existing secret image sharing schemes do not allow processing of a compressed image in the hidden domain. In this paper, we propose a scheme that can scale and crop a CDF (Cohen Daubechies Feauveau) wavelet-based compressed image (such as JPEG2000) in the encrypted domain by smartly applying secret sharing on the wavelet coefficients. Results and analyses show that our scheme is highly secure and has acceptable computational and data overheads.
Mohanty, M, Gehrmann, C & Atrey, PK 2015, 'Avoiding weak parameters in secret image sharing', 2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014, pp. 506-509.View/Download from: Publisher's site
© 2014 IEEE.. © 2014 IEEE. Secret image sharing is a popular image hiding scheme that typically uses (3, 3, n) multi-secret sharing to hide the colors of a secret image. The use of (3, 3, n) multi-secret sharing, however, can lead to information loss. In this paper, we study this loss of information from an image perspective, and show that one-third of the color values of the secret image can be leaked when the sum of any two selected share numbers is equal to the considered prime number in the secret sharing. Furthermore, we show that if the selected share numbers do not satisfy this condition (for example, when the value of each of the selected share number is less than the half of the value of the prime number), then the colors of the secret image are not leaked. In this case, a noise-like image is reconstructed from the knowledge of less than three shares.
Prasad, M, Er, MJ, Lin, C-T, Prasad, OK, Mohanty, M & Singh, J 2015, 'Novel Data Knowledge Representation with TSK-type Preprocessed Collaborative Fuzzy Rule based System', 2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), IEEE Symposium Series Computational Intelligence, IEEE, Cape Town, SOUTH AFRICA, pp. 14-21.View/Download from: Publisher's site
Mohanty, M, Ooi, WT & Atrey, PK 2013, 'Scale me, crop me, knowme not: Supporting scaling and cropping in secret image sharing', Proceedings - IEEE International Conference on Multimedia and Expo.View/Download from: Publisher's site
Secret image sharing is a method for distributing a secret image amongst n data stores, each storing a shadow image of the secret, such that the original secret image can be recovered only if any k out of the n shares is available. Existing secret image sharing schemes, however, do not support scaling and cropping operations on the shadow image, which are useful for zooming on large images. In this paper, we propose an image sharing scheme that allows the user to retrieve a scaled or cropped version of the secret image by operating directly on the shadow images, therefore reducing the amount of data sent from the data stores to the user. Results and analyses show that our scheme is highly secure, requires low computational cost, and supports a large number of scale factors with arbitrary crop. © 2013 IEEE.
Mohanty, M, Ooi, WT & Atrey, PK 2013, 'Secure cloud-based volume ray-casting', Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, pp. 531-538.View/Download from: Publisher's site
Advances in cloud computing have allowed volume rendering tasks, typically done by volume ray-casting, to be outsourced to cloud data centers. The availability of volume data and rendered images (which can contain important information such as the disease information of a patient) to a third-party cloud provider, however, presents security and privacy challenges. This paper addresses these challenges by proposing a secure cloud-based volume ray-casting framework that distributes the rendering tasks among the data centers and hides the information that is exchanged between the server and a data center, between two data centers, and between a data center and the client by using Shamir's secret sharing, such that none of the data centers has enough information to know the secret data and/or rendered image. Experiments and analyses show that our framework is highly secure and requires low computation cost. © 2013 IEEE.
Mohanty, M & Ooi, WT 2012, 'Histopathology image streaming', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 534-545.View/Download from: Publisher's site
This paper proposes an image streaming framework to stream histopathology image of a patient over a lossy network. Firstly, the large histopathology image is divided into a number of fixed size tiles to facilitate ROI-based streaming. Secondly, each tile is compressed using a variant of WebP so that the size of the compressed data is 20% to 30% less than the size of the compressed data when the same tile is compressed using JPEG. Finally, a greedy packetization scheme is proposed to pack the inter-dependent macroblocks of any compressed tile so that the client is able to decode more number of macroblocks than the naive method at any intermediate stage of streaming. © 2012 Springer-Verlag.
Mohanty, M, Atrey, P & Ooi, WT 2012, 'Secure cloud-based medical data visualization', MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia, pp. 1105-1108.View/Download from: Publisher's site
Outsourcing the tasks of medical data visualization to cloud centers presents new security challenges. In this paper, we propose a framework for cloud-based remote medical data visualization that protects the security of data at the cloud centers. To achieve this, we integrate the cryptographic secret sharing with pre-classification volume ray-casting and propose a secure volume ray-casting pipeline that hides the color-coded information of the secret medical data during rendering at the data centers. Results and analysis show the utility of the proposed framework. © 2012 ACM.