scholarly journals A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy

Epilepsia ◽  
2019 ◽  
Vol 60 (4) ◽  
pp. 783-791 ◽  
Author(s):  
Pablo M. Casillas‐Espinosa ◽  
Armen Sargsyan ◽  
Dmitri Melkonian ◽  
Terence J. O'Brien
Keyword(s):  
Rodent Models ◽  
Genetic Epilepsy ◽  
Reliable Detection ◽  
Automated Tool

164: Are Gabab Receptors Involved in Gabapentin Inhibition of Micturition Hyperreflexia in Rodent Models?

2005 ◽  
Vol 173 (4S) ◽  
pp. 44-45
Author(s):  
Quan-Ming Zhu ◽  
Dong-Qing Hu ◽  
David R. Blue ◽  
Philip A. Nunn ◽  
Anthony P.D.W. Ford
Keyword(s):  
Gabab Receptors ◽  
Rodent Models

1216: Oral Treatment with a Vitamin D3 Analogue Shows Anti Inflammatory Effects and Reduces Bladder Overactivity in Rodent Models of Chronic Cystitis

2005 ◽  
Vol 173 (4S) ◽  
pp. 330-330
Author(s):  
Peter Zvara ◽  
Fabio Benigni ◽  
Enrico Baroni ◽  
Marija Zecevic ◽  
Antonia Monno ◽  
...  
Keyword(s):  
Vitamin D3 ◽  
Oral Treatment ◽  
Rodent Models ◽  
Chronic Cystitis ◽  
Anti Inflammatory

Two Faces of Genetic Epilepsy Caused by a Mutation in the SCN8A Gene

2014 ◽  
Vol 45 (S 01) ◽  
Author(s):  
W. Fazeli ◽  
A. Neu ◽  
K. Wenner ◽  
J. Wickert ◽  
J. Johannsen ◽  
...  
Keyword(s):  
Genetic Epilepsy

Detection of Malicious Spatial-Domain Steganography over Noisy Channels Using Convolutional Neural Networks

2020 ◽  
Vol 2020 (4) ◽  
pp. 76-1-76-7
Author(s):  
Swaroop Shankar Prasad ◽  
Ofer Hadar ◽  
Ilia Polian
Keyword(s):  
State Of The Art ◽  
Visual Quality ◽  
Channel Noise ◽  
Detection Accuracy ◽  
Noisy Channel ◽  
Noisy Channels ◽  
Reliable Transmission ◽  
Reliable Detection ◽  
Natural Noise ◽  
Will Force

Image steganography can have legitimate uses, for example, augmenting an image with a watermark for copyright reasons, but can also be utilized for malicious purposes. We investigate the detection of malicious steganography using neural networkbased classification when images are transmitted through a noisy channel. Noise makes detection harder because the classifier must not only detect perturbations in the image but also decide whether they are due to the malicious steganographic modifications or due to natural noise. Our results show that reliable detection is possible even for state-of-the-art steganographic algorithms that insert stego bits not affecting an image’s visual quality. The detection accuracy is high (above 85%) if the payload, or the amount of the steganographic content in an image, exceeds a certain threshold. At the same time, noise critically affects the steganographic information being transmitted, both through desynchronization (destruction of information which bits of the image contain steganographic information) and by flipping these bits themselves. This will force the adversary to use a redundant encoding with a substantial number of error-correction bits for reliable transmission, making detection feasible even for small payloads.

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