scholarly journals A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2742
Author(s):  
Yuwei Ge ◽  
Tao Zhang ◽  
Haihua Liang ◽  
Qingfeng Jiang ◽  
Dan Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Detection Performance ◽  
Hidden Information ◽  
Adaptive Steganography ◽  
Image Steganalysis ◽  
Channel Correlation ◽  
Cyberspace Security ◽  
And Training ◽  
Training Efficiency ◽  
Good Detection

Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural networks (CNNs) can effectively extract the features describing the image content, the difficulty lies in extracting the subtle features that describe the existence of hidden information. Considering this concern, this paper introduces separable convolution and adversarial mechanism, and proposes a new network structure that effectively solves the problem. The separable convolution maximizes the residual information by utilizing its channel correlation. The adversarial mechanism makes the generator extract more content features to mislead the discriminator, thus separating more steganographic features. We conducted experiments on BOSSBase1.01 and BOWS2 to detect various adaptive steganography algorithms. The experimental results demonstrate that our method extracts the steganographic features effectively. The separable convolution increases the signal-to-noise ratio, maximizes the channel correlation of residuals, and improves efficiency. The adversarial mechanism can separate more steganographic features, effectively improving the performance. Compared with the traditional steganalysis methods based on deep learning, our method shows obvious improvements in both detection performance and training efficiency.

scholarly journals Color image steganalysis based on embedding change probabilities in differential channels

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772091782 ◽  
Author(s):  
Chunfang Yang ◽  
Yuhan Kang ◽  
Fenlin Liu ◽  
Xiaofeng Song ◽  
Jie Wang ◽  
...  
Keyword(s):  
Color Image ◽  
Adaptive Steganography ◽  
Potential Threat ◽  
Sensitive Data ◽  
Image Steganalysis ◽  
Feature Based ◽  
Occurrence Matrix ◽  
Model Features ◽  
Color Image Steganography ◽  
The Internet Of Things

It is a potential threat to persons and companies to reveal private or company-sensitive data through the Internet of Things by the color image steganography. The existing rich model features for color image steganalysis fail to utilize the fact that the content-adaptive steganography changes the pixels in complex textured regions with higher possibility. Therefore, this article proposes a variant of spatial rich model feature based on the embedding change probabilities in differential channels. The proposed feature is extracted from the residuals in the differential channels to reduce the image content information and enhance the stego signals significantly. Then, the embedding change probability of each element in the differential channels is added to the corresponding co-occurrence matrix bin to emphasize the interference of the residuals in textured regions to the improved co-occurrence matrix feature. The experimental results show that the proposed feature can significantly improve the detection performances for the WOW and S-UNIWARD steganography, especially when the payload size is small. For example, when the payload size is 0.05 bpp, the detection errors can be reduced respectively by 5.20% and 4.90% for WOW and S-UNIWARD by concatenating the proposed feature to the color rich model feature CRMQ1.

scholarly journals Compressive Detection Using Sub-Nyquist Radars for Sparse Signals

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Ying Sun ◽  
Jianjun Huang ◽  
Jingxiong Huang ◽  
Li Kang ◽  
Li Lei ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Computation Time ◽  
Detection Performance ◽  
High Signal ◽  
Sparse Signals ◽  
Ratio Test ◽  
Digital Conversion ◽  
Real Time Processing ◽  
Analog To Digital ◽  
Analog To Digital Conversion

This paper investigates the compression detection problem using sub-Nyquist radars, which is well suited to the scenario of high bandwidths in real-time processing because it would significantly reduce the computational burden and save power consumption and computation time. A compressive generalized likelihood ratio test (GLRT) detector for sparse signals is proposed for sub-Nyquist radars without ever reconstructing the signal involved. The performance of the compressive GLRT detector is analyzed and the theoretical bounds are presented. The compressive GLRT detection performance of sub-Nyquist radars is also compared to the traditional GLRT detection performance of conventional radars, which employ traditional analog-to-digital conversion (ADC) at Nyquist sampling rates. Simulation results demonstrate that the former can perform almost as well as the latter with a very small fraction of the number of measurements required by traditional detection in relatively high signal-to-noise ratio (SNR) cases.

scholarly journals Comparative Study on Image Steganography: Knight Tour and Rivest Cipher Four Algorithms

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Yusliza Yusoff ◽  
Tassvini A/P Gunaseharan ◽  
Tassvini A/P Gunaseharan
Keyword(s):  
Signal To Noise Ratio ◽  
Simple Algorithm ◽  
Image Steganography ◽  
Security Risk ◽  
Mathematical Technique ◽  
Hidden Information ◽  
Signal To Noise ◽  
Stego Image ◽  
Current Image ◽  
Image Format

Image steganography is a process of hiding message behind an image file which focuses on protecting the existence of a message secret. There is a security risk in the current image steganography process. Since stego-image will be transferred on unsecured Internet network, attackers will attack and try to decode the message behind the stego-image because of the vulnerable algorithm. Therefore, it is very important to search for a method to make the process of encoding the stego-image more secure. There are many algorithms developed to make the stego-image become more secured. However, the usage of Knight Tour (KT) and Rivest Cipher Four (RC4) algorithms in image steganography are still insufficient although that the algorithms are claimed to be secured and robust. KT algorithm is an easy mathematical technique that can increase the security of hidden information, meanwhile, RC4 is known as a simple algorithm but systematic in cover image programming. In this paper, the performance of KT and RC4 algorithms are observed to measure the security and robustness of JPG image format. Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are used to observe the image quality to improve the security factor in the stego-image. From the results, it is found that KT generated better performance compared to RC4. 

scholarly journals Comparison of Passive Sonar Narrowband Detection and Wideband Detection Performance

2021 ◽  
Vol 8 (8) ◽  
pp. 27-29
Author(s):  
Tianhao Yang ◽  
◽  
Qiming Ma ◽  
Tuo Chen ◽  
◽  
...  
Keyword(s):  
Target Detection ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Detection Performance ◽  
Line Spectrum ◽  
Actual Performance ◽  
Factors Affecting ◽  
Sea Trial ◽  
Passive Sonar ◽  
Wideband Detection

Conventional passive sonar uses wideband beam energy for target detection. In the case of low signal-to-noise ratio and strong target interference, the performance of wideband energy detection is rapidly degraded. In view of the relatively stable line spectrum of the radiated noise of underwater targets, the performance of narrowband detection and wideband detection is considered. In stable line spectrum underwater target detection, narrowband detection has performance advantages over wideband detection. However, its actual performance has disadvantages such as sensitivity to noise frequency bands. In this paper, based on the sonar equation, the theoretical methods of wideband detection and narrowband detection are analyzed and the main factors affecting detection performance are given. Simulation and sea trial data processing results verify the effectiveness of the method.

scholarly journals Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis

2019 ◽  
Vol 1 (4) ◽  
pp. 1084-1099
Author(s):  
Luyun Gan ◽  
Brosnan Yuen ◽  
Tao Lu
Keyword(s):  
Spectroscopic Analysis ◽  
Signal To Noise Ratio ◽  
Training Sample ◽  
Gas Mixture ◽  
Infrared Absorption Spectroscopy ◽  
Signal To Noise ◽  
Training Sample Size ◽  
Cluttered Environment ◽  
Binary Relevance ◽  
And Training

In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multiple gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance-partial least squares discriminant analysis when the signal-to-noise ratio and training sample size are sufficient.

scholarly journals Performance of Narrowband Signal Detection under Correlated Rayleigh Fading Based on Synthetic Array

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Ali Broumandan ◽  
John Nielsen ◽  
Gérard Lachapelle
Keyword(s):  
Antenna Array ◽  
Rayleigh Fading ◽  
Signal To Noise Ratio ◽  
Probability Of Detection ◽  
Design Parameters ◽  
Channel Correlation ◽  
Probability Of False Alarm ◽  
Narrowband Signal ◽  
Processing Gain ◽  
Cdma 2000

The performance of a single moving antenna receiver in detecting a narrowband signal under correlated Rayleigh fading is considered. The spatial motion of the antenna during signal capture provides a realization of a synthetic antenna array. As shown, there is a net processing gain obtained by using a synthetic antenna array compared to the equivalent static antenna in Rayleigh fading environments subject to constant processing time. The performance analysis is based on average Signal-to-Noise Ratio (SNR) metrics for design parameters of probability of detection(Pd)and probability of false alarm(Pfa). An optimum detector based on Estimator-Correlator (EC) is developed, and its performance is compared with that of suboptimal Equal-Gain (EG) combiner in different channel correlation scenarios. It is shown that in moderate channel correlation scenarios the detection performance of EC and EG is identical. The sensitivity of the proposed method to knowledge of motion parameters is also investigated. An extensive set of measurements based on CDMA-2000 pilot signals using the static antenna and synthetic array are used to experimentally verify these theoretical findings.

scholarly journals Parameter Estimation and Target Detection of Phased-MIMO Radar Using Capon Estimator

2020 ◽  
Vol 20 (2) ◽  
pp. 60
Author(s):  
Syahfrizal Tahcfulloh ◽  
Muttaqin Hardiwansyah
Keyword(s):  
Parameter Estimation ◽  
False Alarm ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Detection Performance ◽  
Probability Of Detection ◽  
Detection Accuracy ◽  
Adaptive Estimator ◽  
Probability Of False Alarm

Phased-Multiple Input Multiple Output (PMIMO) radar is multi-antenna radar that combines the main advantages of the phased array (PA) and the MIMO radars. The advantage of the PA radar is that it has a high directional coherent gain making it suitable for detecting distant and small radar cross-section (RCS) targets. Meanwhile, the main advantage of the MIMO radar is its high waveform diversity gain which makes it suitable for detecting multiple targets. The combination of these advantages is manifested by the use of overlapping subarrays in the transmit (Tx) array to improve the performance of parameters such as angle resolution and detection accuracy at amplitude and phase proportional to the maximum number of detectable targets. This paper derives a parameter estimation formula with Capon's adaptive estimator and evaluates it for the performance of these parameters. Likewise, derivation for expressions of detection performance such as the probability of false alarm and the probability of detection is also given. The effectiveness and validation of its performance are compared to conventional estimator for other types of radars in terms of the effect of the number of target angles, the RCS of targets, and variations in the number of subarrays at Tx of this radar. Meanwhile, the detection performance is evaluated based on the effect of Signal to Noise Ratio (SNR) and the number of subarrays at Tx. The evaluation results of the estimator show that it is superior to the conventional estimator for estimating the parameters of this radar as well as the detection performance. Having no sidelobe makes this estimator strong against the influence of interference and jamming so that it is suitable and attractive for the design of radar systems. Root mean square error (RMSE) on magnitude detection from LS and Capon estimators were 0.033 and 0.062, respectively. Meanwhile, the detection performance for this radar has the probability of false alarm above 10-4 and the probability of detection of more than 99%.

scholarly journals Increasing pupil size is associated with improved detection performance in the periphery

2021 ◽  
Author(s):  
Lisa Valentina Eberhardt ◽  
Christoph Strauch ◽  
Tim Samuel Hartmann ◽  
Anke Huckauf
Keyword(s):  
Visible Light ◽  
Pupil Size ◽  
Signal To Noise Ratio ◽  
Detection Performance ◽  
Eye Tracker ◽  
Signal To Noise ◽  
Current Trial ◽  
Spatial Frequencies ◽  
Online Setting ◽  
Pupillary Change

AbstractVisible light enters our body via the pupil. By changing its size, the pupil shapes visual input. Small apertures increase the resolution of high spatial frequencies, thus allowing discrimination of fine details. Large apertures, in contrast, provide a better signal-to-noise ratio, because more light can enter the eye. This should lead to better detection performance of peripheral stimuli. Experiment 1 shows that the effect can reliably be demonstrated even in a less controlled online setting. In Experiment 2, pupil size was measured in a laboratory using an eye tracker. The findings replicate findings showing that large pupils provide an advantage for peripheral detection of faint stimuli. Moreover, not only pupil size during information intake in the current trial n, but also its interaction with pupil size preceding information intake, i.e., in trial n-1, predicted performance. This suggests that in addition to absolute pupil size, the extent of pupillary change provides a mechanism to modulate perceptual functions. The results are discussed in terms of low-level sensory as well as higher-level arousal-driven changes in stimulus processing.

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