Search Results for author:
Found 9 papers, 4 papers with code
Automatic coral reef fish identification and 3D measurement in the wild
In this paper we present a pipeline using stereo images in order to automatically identify, track in 3D fish, and measure fish population.
Astronomical image time series classification using CONVolutional attENTION (ConvEntion)
In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.
A study on the invariance in security whatever the dimension of images for the steganalysis by deep-learning
In this paper, we study the performance invariance of convolutional neural networks when confronted with variable image sizes in the context of a more "wild steganalysis".
LSSD: a Controlled Large JPEG Image Database for Deep-Learning-based Steganalysis "into the Wild"
In this paper, we describe a large JPEG database composed of 2 million colour and grey-scale images.
Cryptography and Security
Deep Learning in steganography and steganalysis from 2015 to 2018
For almost 10 years, the detection of a hidden message in an image has been mainly carried out by the computation of Rich Models (RM), followed by classification using an Ensemble Classifier (EC).
Cryptography and Security
A CNN adapted to time series for the classification of Supernovae
The first one is adapted to time series and thus to the treatment of supernovae light-curves.
Yedrouj-Net: An efficient CNN for spatial steganalysis
For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features.
How to augment a small learning set for improving the performances of a CNN-based steganalyzer?
To our knowledge, no study has been performed on the enrichment impact of a learning database on the steganalysis performance.
Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch
In this paper, we follow-up the study of Qian et al., and show that, due to intrinsic joint minimization, the results obtained from a Convolutional Neural Network (CNN) or a Fully Connected Neural Network (FNN), if well parameterized, surpass the conventional use of a RM with an EC.
