Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain

Tabares-Soto, Reinel; Arteaga-Arteaga, Harold Brayan; Mora-Rubio, Alejandro; Bravo-Ortíz, Mario Alejandro; Arias-Garzón, Daniel; Alzate-Grisales, Jesús Alejandro; Jacome, Alejandro Burbano; Orozco-Arias, Simón; Isaza, Gustavo; Pollán, Raúl Ramos

doi:10.7717/peerj-cs.451

Cited by 15 publications

(13 citation statements)

References 32 publications

(64 reference statements)

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“…Two automatic steps of CNNs are feature extraction and classification (Tabares-Soto et al, 2021, Al-yousif et al, 2021. One of the main factors of performing an excellent classification is the extracted features.…”

Section: Feature Extraction In the Proposed Modelmentioning

confidence: 99%

Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study

Alzubaidi

Duan

Al-Dujaili

et al. 2021

PeerJ Computer Science

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Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pre-trained models have shown an effective performance in several domains of application, those models may not offer significant benefits in all instances when dealing with medical imaging scenarios. Such models were designed to classify a thousand classes of natural images. There are fundamental differences between these models and those dealing with medical imaging tasks regarding learned features. Most medical imaging applications range from two to ten different classes, where we suspect that it would not be necessary to employ deeper learning models. This paper investigates such a hypothesis and develops an experimental study to examine the corresponding conclusions about this issue. The lightweight convolutional neural network (CNN) model and the pre-trained models have been evaluated using three different medical imaging datasets. We have trained the lightweight CNN model and the pre-trained models with two scenarios which are with a small number of images once and a large number of images once again. Surprisingly, it has been found that the lightweight model trained from scratch achieved a more competitive performance when compared to the pre-trained model. More importantly, the lightweight CNN model can be successfully trained and tested using basic computational tools and provide high-quality results, specifically when using medical imaging datasets.

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Section: Feature Extraction In the Proposed Modelmentioning

confidence: 99%

Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study

Alzubaidi

Duan

Al-Dujaili

et al. 2021

PeerJ Computer Science

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“…This database consists of 10,000 cover images of 512 × 512 pixels in a Portable Gray Map (PGM) format (8 bits grayscale). For this research, similar to the process presented by Tabares-Soto et al (2021), the following operations were performed on the images: All images were resized to 256 × 256 pixels.…”

Section: Databasementioning

confidence: 99%

“…The CNN architectures used in this research, except for GBRAS-Net, were modified according to the strategy described in Tabares-Soto et al (2021) to improve the performance of the networks regarding convergence, stability of the training process, and detection accuracy. The modifications involved the following: a preprocessing stage with 30 SRM filters and a modified TanH activation with range [−3, 3], Spatial Dropout before the convolutional layers, Absolute Value followed by Batch Normalization after the convolutional layers, Leaky ReLU activation in convolutional layers, and a classification stage with three fully connected layers (Bravo Ortíz et al, 2021).…”

Section: Cnn Architecturesmentioning

confidence: 99%

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Sensitivity of deep learning applied to spatial image steganalysis

Tabares-Soto

Arteaga-Arteaga

Mora-Rubio

et al. 2021

PeerJ Computer Science

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In recent years, the traditional approach to spatial image steganalysis has shifted to deep learning (DL) techniques, which have improved the detection accuracy while combining feature extraction and classification in a single model, usually a convolutional neural network (CNN). The main contribution from researchers in this area is new architectures that further improve detection accuracy. Nevertheless, the preprocessing and partition of the database influence the overall performance of the CNN. This paper presents the results achieved by novel steganalysis networks (Xu-Net, Ye-Net, Yedroudj-Net, SR-Net, Zhu-Net, and GBRAS-Net) using different combinations of image and filter normalization ranges, various database splits, different activation functions for the preprocessing stage, as well as an analysis on the activation maps and how to report accuracy. These results demonstrate how sensible steganalysis systems are to changes in any stage of the process, and how important it is for researchers in this field to register and report their work thoroughly. We also propose a set of recommendations for the design of experiments in steganalysis with DL.

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“…Various methods are used to measure the success of steganography methods and to detect stego-data as it is not realistic for humans to detect and analyze the steganography methods used today. For this reason, many computerized steganalysis methods are used to detect steganography-applied multimedia stego-data [20].…”

Section: Introductionmentioning

confidence: 99%

A Novel and Robust Hybrid Blockchain and Steganography Scheme

et al. 2021

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Data security and data hiding have been studied throughout history. Studies show that steganography and encryption methods are used together to hide data and avoid detection. Large amounts of data hidden in the cover multimedia distort the image, which can be detected in visual and histogram analysis. The proposed method will solve two major drawbacks of the current methods: the limitation imposed on the size of the data to be hidden in the cover multimedia and low resistance to steganalysis after stego-operation. In the proposed method, plaintext data are divided into fixed-sized bits whose corresponding matching bits’ indices in the cover multimedia are accumulated. Thus, the hidden data are composed of the indices in the cover multimedia, causing no change in it, thus enabling considerable amounts of plaintext to be hidden. The proposed method also has high resistance to known steganalysis methods because it does not cause any distortion to the cover multimedia. The test results show that the performance of the proposed method outperforms similar conventional stenographic techniques. The proposed Ozyavas–Takaoglu–Ajlouni (OTA) method relieves the limitation on the size of the hidden data, and hidden data is undetectable by steganalysis because it is no longer embedded in the cover multimedia.

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Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain

Cited by 15 publications

References 32 publications

Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study

Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study

Sensitivity of deep learning applied to spatial image steganalysis

A Novel and Robust Hybrid Blockchain and Steganography Scheme

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