Blind image watermark detection algorithm based on discrete shearlet transform using statistical decision theory

Journal article


Ahmaderaghi, Baharak, Kurugollu, Fatih, Rincon, Jesus Martinez Del and Bouridane, Ahmed 2018. Blind image watermark detection algorithm based on discrete shearlet transform using statistical decision theory. IEEE Transactions on Computational Imaging. https://doi.org/10.1109/TCI.2018.2794065
AuthorsAhmaderaghi, Baharak, Kurugollu, Fatih, Rincon, Jesus Martinez Del and Bouridane, Ahmed
Abstract

Blind watermarking targets the challenging recovery of the watermark when the host is not available during the detection stage.This paper proposes Discrete Shearlet Transform (DST) as a new embedding domain for blind image watermarking. Our novel DST blind watermark detection system uses a non-additive scheme based on the statistical decision theory. It first computes the Probability Density Function (PDF) of the DST coefficients modelled as a Laplacian distribution. The resulting likelihood ratio is compared with a decision threshold calculated using Neyman-Pearson criterion to minimise the missed detection subject to a fixed false alarm probability. Our method is evaluated in terms of imperceptibility, robustness and payload against different attacks (Gaussian noise, Blurring, Cropping, Compression and Rotation) using 30 standard grayscale images covering different characteristics (smooth, more complex with a lot of edges and high detail textured regions). The proposed method shows greater windowing flexibility with more sensitive to directional and anisotropic features when compared against Discrete Wavelet and Contourlets.

KeywordsDigital image watermarking; Frequency domain; DST; Discrete Wavelet Transform; Contourlet Transform; Laplacian distribution
Year2018
JournalIEEE Transactions on Computational Imaging
PublisherIEEE
ISSN2333-9403
2334-0118
Digital Object Identifier (DOI)https://doi.org/10.1109/TCI.2018.2794065
Web address (URL)http://hdl.handle.net/10545/623626
hdl:10545/623626
Publication dates15 Jan 2018
Publication process dates
Deposited20 Mar 2019, 11:49
Accepted03 Jan 2018
Rights

Archived with thanks to IEEE Transactions on Computational Imaging

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ContributorsQueen's University, Belfast
File
File Access Level
Open
File
File Access Level
Open
Permalink -

https://repository.derby.ac.uk/item/9396q/blind-image-watermark-detection-algorithm-based-on-discrete-shearlet-transform-using-statistical-decision-theory

Download files

  • 58
    total views
  • 38
    total downloads
  • 5
    views this month
  • 6
    downloads this month

Export as

Related outputs

Deep labeller: automatic bounding box generation for synthetic violence detection datasets
Nadeem, M., Kurugollu, F., Saravi, S., Atlam, H. and Franqueira, V. 2023. Deep labeller: automatic bounding box generation for synthetic violence detection datasets. Multimedia Tools and Applications. pp. 1-18. https://doi.org/10.1007/s11042-023-15621-5
Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image
Tengku Mohd Afendi, Zulcaffle,, Kurugollu, F., Kuryati, K., Joseph, A. and Bong, D. L. 2023. Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image. International Journal of Computing and Digital Systems. 2, pp. 1-10. https://doi.org/10.12785/ijcds/XXXXXX
Explaining deep neural networks: A survey on the global interpretation methods
Saleem, R., Yuan, B., Kurugollu, F., Anjum, A. and Liu, L. 2022. Explaining deep neural networks: A survey on the global interpretation methods. Neurocomputing. 513, pp. 165-180. https://doi.org/10.1016/j.neucom.2022.09.129
Severity Estimation of Plant Leaf Diseases Using Segmentation Method
Entuni, Chyntia Jaby, Afendi Zulcaffle, Tengku Mohd, Kipli, Kuryati and Kurugollu, Fatih 2020. Severity Estimation of Plant Leaf Diseases Using Segmentation Method. Applied Science and Engineering Progress. 14 (1), pp. 108-119. https://doi.org/10.14416/j.asep.2020.11.004
Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks
Saleem, Rabia, Yuan, Bo, Kurugollu, Fatih and Anjum, Ashiq 2020. Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks. IEEE. https://doi.org/10.1109/ucc48980.2020.00070
NOTRINO: a NOvel hybrid TRust management scheme for INternet-Of-vehicles
Ahmad, F., Kurugollu, Fatih, Kerrache, Chaker Abdelaziz, Sezer, Sakir and Liu, Lu 2021. NOTRINO: a NOvel hybrid TRust management scheme for INternet-Of-vehicles. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2021.3049189
A Novel Security Methodology for Smart Grids: A Case Study of Microcomputer-Based Encryption for PMU Devices
Varan, Metin, Akgul, Akif, Kurugollu, Fatih, Sansli, Ahmet and Smith, K. 2021. A Novel Security Methodology for Smart Grids: A Case Study of Microcomputer-Based Encryption for PMU Devices. Complexity. 2021, pp. 1-15. https://doi.org/10.1155/2021/2798534
Persation: an IoT based personal safety prediction model aided solution
Alofe, Olasunkanmi Matthew, Fatema, Kaniz, Azad, Muhammad Ajmal and Kurugollu, Fatih 2020. Persation: an IoT based personal safety prediction model aided solution. International Journal of Computing and Digital Systems.
MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles
Ahmad, F., Kurugollu, Fatih, Adnane, Asma, Hussain, Rasheed and Hussain, Fatima 2020. MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles. IEEE Internet of Things. https://doi.org/10.1109/JIOT.2020.2967568
CRT-BIoV: A cognitive radio technique for blockchain-enabled internet of vehicles
Rathee, Geetanjali, Ahmad, F., Kurugollu, Fatih, Azad, Muhammad, Iqbal, Razi and Imran, Muhammad 2020. CRT-BIoV: A cognitive radio technique for blockchain-enabled internet of vehicles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2020.3004718
Vehicular sensor networks: Applications, advances and challenges
Kurugollu, Fatih, Ahmed, Syed Hassan, Hussain, Rasheed, Ahmad, F. and Kerrache, Chaker Abdelaziz 2020. Vehicular sensor networks: Applications, advances and challenges. Sensors. https://doi.org/10.3390/s20133686
Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes
Tasdemir, Kasim, Kurugollu, Fatih and Sezer, Sakir 2016. Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2016.2567073
Cascaded multimodal biometric recognition framework
Albesher, Badr, Kurugollu, Fatih, Bouridane, Ahmed and Baig, Asim 2013. Cascaded multimodal biometric recognition framework. IET Biometrics. https://doi.org/10.1049/iet-bmt.2012.0043
Privacy region protection for H.264/AVC with enhanced scrambling effect and a low bitrate overhead
Wang, Yongsheng, O׳Neill, Máire, Kurugollu, Fatih and O׳Sullivan, Elizabeth 2015. Privacy region protection for H.264/AVC with enhanced scrambling effect and a low bitrate overhead. Signal Processing: Image Communication. https://doi.org/10.1016/j.image.2015.04.013
Frontal view gait recognition with fusion of depth features from a time of flight camera
Afendi Tengku Mohd, Kurugollu, Fatih, Crookes, Danny, Bouridane, Ahmed and Farid, Mohsen 2018. Frontal view gait recognition with fusion of depth features from a time of flight camera. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/TIFS.2018.2870594
Towards a trusted unmanned aerial system using blockchain (BUAS) for the protection of critical infrastructure
Barka, Ezedin, Kerrache, Chaker Abdelaziz, Benkraouda, Hadjer, Shuaib, Khaled, Ahmad, F. and Kurugollu, Fatih 2019. Towards a trusted unmanned aerial system using blockchain (BUAS) for the protection of critical infrastructure. Transactions on Emerging Telecommunications Technologies. https://doi.org/10.1002/ett.3706
A comparative analysis of trust models for safety applications in IoT-enabled vehicular networks
Ahmad, F., Adnane, Asma, Hussain, Rasheed and Kurugollu, Fatih 2019. A comparative analysis of trust models for safety applications in IoT-enabled vehicular networks. IEEE.
A survey of deep learning solutions for multimedia visual content analysis.
Nadeem, Muhammad Shahroz, Franqueira, Virginia N. L., Zhai, Xiaojun and Kurugollu, Fatih 2019. A survey of deep learning solutions for multimedia visual content analysis. IEEE Access. https://doi.org/10.1109/ACCESS.2019.DOI
Realization of blockchain in named data networking-based internet-of-vehicles
Ahmad, F., Kerrache, Chaker Abdelaziz, Kurugollu, Fatih and Hussain, Rasheed 2019. Realization of blockchain in named data networking-based internet-of-vehicles. IT Professional. https://doi.org/10.1109/MITP.2019.2912142
Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes.
Tasdemir, Kasim, Kurugollu, Fatih and Sezer, Sakir 2016. Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2016.2567073
Man-In-The-Middle attacks in Vehicular Ad-Hoc Networks: Evaluating the impact of attackers’ strategies.
Ahmad, F., Adnane, Asma, Franqueira, Virginia N. L., Kurugollu, Fatih and Liu, Lu 2018. Man-In-The-Middle attacks in Vehicular Ad-Hoc Networks: Evaluating the impact of attackers’ strategies. Sensors. 18 (11), p. 4040. https://doi.org/10.3390/s18114040
Digital video source identification based on green-channel photo response non-uniformity (G-PRNU)
Al-Athamneh, Mohammad, Kurugollu, Fatih, Crookes, Danny and Farid, Mohsen 2016. Digital video source identification based on green-channel photo response non-uniformity (G-PRNU). https://doi.org/10.5121/csit.2016.61105