Enhancing Secret Data Detection Using Convolutional Neural Networks With Fuzzy Edge Detection

Croix, Ntivuguruzwa Jean De La; Ahmad, Tohari; Han, Fengling

doi:10.1109/access.2023.3334650

Cited by 13 publications

(1 citation statement)

References 58 publications

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“…The primary advantage conferred by this function lies in its ability to generate a zero-centered output, thereby facilitating the backpropagation process [22,23]. In this preprocessing layer, the weights are designated as non-trainable, signifying that throughout the training phase, these weights remain static and are not subject to updates or evaluations.…”

Section: Figure 2 Architecture Of the Proposed Cnnmentioning

confidence: 99%

Detecting Hidden Data in Images Using Convolutional Neural Networks

Delenia,

Putra,

Triwibowo

et al. 2024

MMEP

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Recent advancements in Deep Learning (DL) have driven the development of innovative methodologies, particularly within the domain of steganalysis for spatial domain images. Steganalysis, as the counterpart to steganography, is dedicated to uncovering concealed data within the content, making a digital image. Convolutional Neural Networks (CNNs), grounded in DL principles, have been influential in pushing the boundaries of this field. Despite the development of various CNN architectures that have raised the precision in detecting images with steganographic payload, current models contend with challenges related to the detectability of low payload capacities and suboptimal processes for feature learning. In response, this study introduces a novel CNN architecture to enhance steganalysis and improve the accuracy of detecting covert data in spatial domain images. The proposed model introduces a strategic integration of maximum and average pooling, a tandem approach meticulously designed to amplify the network's proficiency in capturing intricate details and multiple layers of information. Moreover, the proposed CNN architecture is structured into three principal stages: preprocessing, feature extraction, and classification. The preprocessing stage comprises Input, regular convolution layer, and Batch Normalization. The feature extraction stage employs the ReLU as a non-linear activation function based on its capacity to expedite computation by bypassing the need for exponentials and divisions. The classification stage introduces the multi-scale inception module to enhance the probabilistic feature classification. The proposed model's correctness in probabilistic classification through the receiver operating characteristic curve (ROC AUC) yields an AUC of 0.95, reflecting a prediction correctness of 95%. Furthermore, the results show that the proposed model outperforms the results of previous research studies in terms of accuracy and improves the existing works with a percentage ranging from 2.3 to 2.9%.

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Section: Figure 2 Architecture Of the Proposed Cnnmentioning

confidence: 99%