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Agrawal R, Ahuja K, Steinbach MC, Wick T. SABMIS: sparse approximation based blind multi-image steganography scheme. PeerJ Comput Sci 2022; 8:e1080. [PMID: 36532802 PMCID: PMC9748825 DOI: 10.7717/peerj-cs.1080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 08/08/2022] [Indexed: 06/17/2023]
Abstract
We hide grayscale secret images into a grayscale cover image, which is considered to be a challenging steganography problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stego-image as well as the extracted secret image, and ensuring that the stego-image is resistant to steganographic attacks. The novel embedding rule of our scheme helps to hide secret image sparse coefficients into the oversampled cover image sparse coefficients in a staggered manner. The stego-image is constructed by using the Alternating Direction Method of Multipliers (ADMM) to solve the Least Absolute Shrinkage and Selection Operator (LASSO) formulation of the underlying minimization problem. Finally, the secret images are extracted from the constructed stego-image using the reverse of our embedding rule. Using these components together, to achieve the above mentioned competing goals, forms our most novel contribution. We term our scheme SABMIS (Sparse Approximation Blind Multi-Image Steganography). We perform extensive experiments on several standard images. By choosing the size of the length and the width of the secret images to be half of the length and the width of cover image, respectively, we obtain embedding capacities of 2 bpp (bits per pixel), 4 bpp, 6 bpp, and 8 bpp while embedding one, two, three, and four secret images, respectively. Our focus is on hiding multiple secret images. For the case of hiding two and three secret images, our embedding capacities are higher than all the embedding capacities obtained in the literature until now (3 times and 6 times than the existing best, respectively). For the case of hiding four secret images, although our capacity is slightly lower than one work (about 2/3rd), we do better on the other two goals (quality of stego-image & extracted secret image as well as resistance to steganographic attacks). For our experiments, there is very little deterioration in the quality of the stego-images as compared to their corresponding cover images. Like all other competing works, this is supported visually as well as over 30 dB of Peak Signal-to-Noise Ratio (PSNR) values. The good quality of the stego-images is further validated by multiple numerical measures. None of the existing works perform this exhaustive validation. When using SABMIS, the quality of the extracted secret images is almost same as that of the corresponding original secret images. This aspect is also not demonstrated in all competing literature. SABMIS further improves the security of the inherently steganographic attack resistant transform based schemes. Thus, it is one of the most secure schemes among the existing ones. Additionally, we demonstrate that SABMIS executes in few minutes, and show its application on the real-life problems of securely transmitting medical images over the internet.
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Affiliation(s)
- Rohit Agrawal
- Computer Science and Engineering, Indian Institute of Technology Indore, Indore, India
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India
| | - Kapil Ahuja
- Computer Science and Engineering, Indian Institute of Technology Indore, Indore, India
| | - Marc C. Steinbach
- Leibniz Universität Hannover, Institut für Angewandte Mathematik, Hannover, Germany
| | - Thomas Wick
- Leibniz Universität Hannover, Institut für Angewandte Mathematik, Hannover, Germany
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Jalaeian Zaferani E, Teshnehlab M, Khodadadian A, Heitzinger C, Vali M, Noii N, Wick T. Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception. Sensors (Basel) 2022; 22:s22166206. [PMID: 36015967 PMCID: PMC9413006 DOI: 10.3390/s22166206] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 08/15/2022] [Accepted: 08/16/2022] [Indexed: 05/27/2023]
Abstract
In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage. To address this challenge, researchers have applied optimization methods. Although there were successes, the search space is very large due to the large number of deep learning hyper-parameters, which increases the probability of getting stuck in local optima. Researchers have also focused on improving global optimization methods. In this regard, we suggest a novel global optimization method based on the cultural algorithm, multi-island and the concept of parallelism to search this large space smartly. At first, we evaluated our method on three well-known optimization benchmarks and compared the results with recently published papers. Results indicate that the convergence of the proposed method speeds up due to the ability to escape from local optima, and the precision of the results improves dramatically. Afterward, we applied our method to optimize five hyper-parameters of an asymmetric auto-encoder for automatic personality perception. Since inappropriate hyper-parameters lead the network to over-fitting and under-fitting, we used a novel cost function to prevent over-fitting and under-fitting. As observed, the unweighted average recall (accuracy) was improved by 6.52% (9.54%) compared to our previous work and had remarkable outcomes compared to other published personality perception works.
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Affiliation(s)
- Effat Jalaeian Zaferani
- Electrical & Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
| | - Mohammad Teshnehlab
- Electrical & Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
| | - Amirreza Khodadadian
- Institute of Applied Mathematics, Leibniz University of Hannover, 30167 Hannover, Germany
| | - Clemens Heitzinger
- Institute of Analysis and Scientific Computing, TU Wien, 1040 Vienna, Austria
- Center for Artificial Intelligence and Machine Learning (CAIML), TU Wien, 1040 Vienna, Austria
| | - Mansour Vali
- Electrical & Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
| | - Nima Noii
- Institute of Continuum Mechanics, Leibniz University of Hannover, 30823 Garbsen, Germany
| | - Thomas Wick
- Institute of Applied Mathematics, Leibniz University of Hannover, 30167 Hannover, Germany
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Abstract
Abstract
In this work, we are concerned with neural network guided goal-oriented a posteriori error estimation and adaptivity using the dual weighted residual method. The primal problem is solved using classical Galerkin finite elements. The adjoint problem is solved in strong form with a feedforward neural network using two or three hidden layers. The main objective of our approach is to explore alternatives for solving the adjoint problem with greater potential of a numerical cost reduction. The proposed algorithm is based on the general goal-oriented error estimation theorem including both linear and nonlinear stationary partial differential equations and goal functionals. Our developments are substantiated with some numerical experiments that include comparisons of neural network computed adjoints and classical finite element solutions of the adjoints. In the programming software, the open-source library deal.II is successfully coupled with LibTorch, the PyTorch C++ application programming interface.
Article Highlights
Adjoint approximation with feedforward neural network in dual-weighted residual error estimation.
Side-by-side comparisons for accuracy and computational cost with classical finite element computations.
Numerical experiments for linear and nonlinear problems yielding excellent effectivity indices.
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von Kodolitsch Y, Prokoph M, Sachweh A, Kölbel T, Detter C, Berger J, Wick T, Debus S, Blankart CR. How military history can inspire medical intervention. Cardiovasc Diagn Ther 2021; 10:2048-2053. [PMID: 33381443 DOI: 10.21037/cdt.2020.03.06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Yskert von Kodolitsch
- German Aorta Center Hamburg at the University Heart and Vascular Center Hamburg, Hamburg, Germany
| | - Martin Prokoph
- German Bundeswehr, US Army Command and General Staff College, Fort Leavenworth, KS, USA
| | - Arnim Sachweh
- German Aorta Center Hamburg at the University Heart and Vascular Center Hamburg, Hamburg, Germany
| | - Tilo Kölbel
- German Aorta Center Hamburg at the University Heart and Vascular Center Hamburg, Hamburg, Germany
| | - Christian Detter
- German Aorta Center Hamburg at the University Heart and Vascular Center Hamburg, Hamburg, Germany
| | - Jürgen Berger
- Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Hospital Eppendorf, Hamburg, Germany
| | - Thomas Wick
- Leibniz University Hannover, Institute of Applied Mathematics (IfAM), Hannover, Germany
| | - Sebastian Debus
- German Aorta Center Hamburg at the University Heart and Vascular Center Hamburg, Hamburg, Germany
| | - Carl Rudolf Blankart
- KPM Center for Public Management, University of Bern, Bern, Switzerland.,sitem-insel AG, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
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Khodadadian A, Noii N, Parvizi M, Abbaszadeh M, Wick T, Heitzinger C. A Bayesian estimation method for variational phase-field fracture problems. Comput Mech 2020; 66:827-849. [PMID: 33029034 PMCID: PMC7510934 DOI: 10.1007/s00466-020-01876-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 06/17/2020] [Indexed: 06/11/2023]
Abstract
In this work, we propose a parameter estimation framework for fracture propagation problems. The fracture problem is described by a phase-field method. Parameter estimation is realized with a Bayesian approach. Here, the focus is on uncertainties arising in the solid material parameters and the critical energy release rate. A reference value (obtained on a sufficiently refined mesh) as the replacement of measurement data will be chosen, and their posterior distribution is obtained. Due to time- and mesh dependencies of the problem, the computational costs can be high. Using Bayesian inversion, we solve the problem on a relatively coarse mesh and fit the parameters. In several numerical examples our proposed framework is substantiated and the obtained load-displacement curves, that are usually the target functions, are matched with the reference values.
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Affiliation(s)
- Amirreza Khodadadian
- Institute of Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8–10, 1040 Vienna, Austria
- Institute of Applied Mathematics, Leibniz University Hannover, Welfengarten 1, 30167 Hanover, Germany
| | - Nima Noii
- Institute of Applied Mathematics, Leibniz University Hannover, Welfengarten 1, 30167 Hanover, Germany
| | - Maryam Parvizi
- Institute of Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8–10, 1040 Vienna, Austria
| | - Mostafa Abbaszadeh
- Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914 Iran
| | - Thomas Wick
- Institute of Applied Mathematics, Leibniz University Hannover, Welfengarten 1, 30167 Hanover, Germany
| | - Clemens Heitzinger
- Institute of Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8–10, 1040 Vienna, Austria
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287 USA
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Mertz DP, Wick T. [Gastric secretion during ouabain medication (author's transl)]. Med Klin 1975; 70:228-30. [PMID: 235715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Mertz DP, Wick T, Thongbhoubesra T, Walloschek C. �ber die akute Wirkung verschiedener pharmakologischer Substanzen auf die S�uresekretion am stimulierten menschlichen Magen. ACTA ACUST UNITED AC 1970. [DOI: 10.1007/bf01494500] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Wick T. [Experiences with a combined glycoside-nitrite treatment in chronic coronary insufficiency]. Med Welt 1969; 42:2327-30. [PMID: 5355678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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Fr�mter E, Wick T, Hegel U. Untersuchungen �ber die Ausspritzmethode zur Lokalisation der Mikroelektrodenspitze bei Potentialmessungen am proximalen Konvolut der Rattenniere. Pflugers Arch 1967. [DOI: 10.1007/bf00363112] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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