Recently, most image classification studies solicit the intervention of convolutional neural networks because these DL-based classification methods generally outperform other methodologies with higher accuracy. However, this type of deep learning networks require many parameters and have a complex structure with multiple convolutional and pooling layers depending on the objective. These layers compute a large volume of data and it may impact the processing time and the performance. Therefore, this paper proposes a new method of image classification based on the light convolutional neural network. It consists of replacing the feature extraction layers of standard convolutional neural network with a single pulse coupled neural network by introducing the notion of foveation. This module provides the feature map of input image and the data compression using Discrete Wavelet Transform which is an optional step depending on the information quantity of this signature. The fully connected neural network, which has six hidden layers, classifies the image. With this technique, the computation time is reduced, and the network architecture is identical and simple independent of the type of dataset. The number of parameter is less than that in current research. The proposed method was validated with different dataset such as Caltech-101, Caltech-256, CIFAR-10, CIFAR-100, and ImageNet, and the accuracy reaches 92%, 90%, 99%, 94%, and 91%, respectively, which are better than the previous related works.
In front of COVID-19 propagation, we can protect our self by taking precautionary measures such as wearing face masks. It may be mandatory in particular public place although some persons ignore this rule. Several research in face mask detection area have emerged and most of studies are based on deep learning. In this paper, we present a method to detect whether person wear a mask or not to prevent the propagation of virus. The approach is based on combination of Pulse Couple Neural Network and Fully Connected Neural Network and the processing is divided in three steps: geometrical, feature extraction and decision. The geometrical module selects the Region of Interest for given image and the feature extraction module composed by Pulse Couple Neural Network extracts all pertinent information which will be used by the last module for decision. This decision module makes directly a decision in case of non-complex classification without neural network training overwise the Fully Connected Neural Network continues the treatment. The input image may be captured from video surveillance sequence, the system triggers a signal alarm once a person doesn’t wear face mask. Our proposed approach was tested with different datasets like Kaggle, AIZOO, Moxa3K, Real-World Masked Face Dataset, Medical Masks Dataset, Face Mask Dataset and the accuracy varies from 83.2% to 100% with minimum computation time.
In front of information fishing, it is necessary to protect the information to be circulated over public channel. The goal of this paper is to propose an approach which inserts a logo inside of cover image and having three different possible results according to user's needs. The algorithm can perform steganography, watermarking or host encryption. This technique is based on singular value decomposition (SVD) and discrete wavelet transform (DWT) composed by three steps: pre‐processing, embedding and extraction/decryption. Reference matrix and reference parameter are the two factors deciding the operation and the intervention of neural networks makes the choice of their parameters easier according to the desired result. For measuring the steganography performance, the authors adopt the structural similarity index measure (SSIM) which calculates the similarity between original and watermarked image and peak signal‐to‐noise ratio. For watermarking, the normalized correlation (NC) coefficient investigates the correlation between the original watermark and the extracted watermark. Attacking the watermarked image with common attacks used by previous publication searchers, the value of SSIM and NC coefficient are closed to one. Regarding peak signal‐to‐noise ratio, the overall score is around 61.73 dB. The performance score is not negligible also for the encryption method.
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