scholarly journals A New Convolutional Neural Network-Based Steganalysis Method for Content-Adaptive Image Steganography in the Spatial Domain

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 47013-47020
Author(s):  
Zhili Xiang ◽  
Jun Sang ◽  
Qian Zhang ◽  
Bin Cai ◽  
Xiaofeng Xia ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spatial Domain ◽  
Image Steganography ◽  
Steganalysis Method ◽  
Content Adaptive

scholarly journals Chicken-Moth Search Optimization-based Deep Convolutional Neural Network for Image Steganography

2020 ◽  
Vol 21 (2) ◽  
pp. 217-232
Author(s):  
Reshma V K ◽  
Vinod Kumar R S ◽  
Shahi D ◽  
Shyjith M B
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Deep Convolutional Neural Network ◽  
Image Steganography ◽  
Secret Message ◽  
Search Optimization ◽  
Tetrolet Transform

Image steganography is considered as one of the promising and popular techniques utilized to maintain the confidentiality of the secret message that is embedded in an image. Even though there are various techniques available in the previous works, an approach providing better results is still the challenge. Therefore, an effective pixel prediction based on image stegonography is developed, which employs error dependent Deep Convolutional Neural Network (DCNN) classifier for pixel identification. Here, the best pixels are identified from the medical image based on DCNN classifier using pixel features, like texture, wavelet energy, Gabor, scattering features, and so on. The DCNN is optimally trained using Chicken-Moth search optimization (CMSO). The CMSO is designed by integrating Chicken Swarm Optimization (CSO) and Moth Search Optimization (MSO) algorithm based on limited error. Subsequently, the Tetrolet transform is fed to the predicted pixel for the embedding process. At last, the inverse tetrolet transform is used for extracting the secret message from an embedded image. The experimentation is carried out using BRATS dataset, and the performance of image stegonography based on CMSO-DCNN+tetrolet is evaluated based on correlation coefficient, Structural Similarity Index, and Peak Signal to Noise Ratio, which attained 0.85, 46.981dB, and 0.6388, for the image with noise.  

scholarly journals Deep Learning Based on Fourier Convolutional Neural Network Incorporating Random Kernels

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2004
Author(s):  
Yuna Han ◽  
Byung-Woo Hong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Activation Function ◽  
Spatial Domain ◽  
Transformation Process ◽  
Real Component ◽  
Rectified Linear Unit ◽  
Classification Tasks

In recent years, convolutional neural networks have been studied in the Fourier domain for a limited environment, where competitive results can be expected for conventional image classification tasks in the spatial domain. We present a novel efficient Fourier convolutional neural network, where a new activation function is used, the additional shift Fourier transformation process is eliminated, and the number of learnable parameters is reduced. First, the Phase Rectified Linear Unit (PhaseReLU) is proposed, which is equivalent to the Rectified Linear Unit (ReLU) in the spatial domain. Second, in the proposed Fourier network, the shift Fourier transform is removed since the process is inessential for training. Lastly, we introduce two ways of reducing the number of weight parameters in the Fourier network. The basic method is to use a three-by-three sized kernel instead of five-by-five in our proposed Fourier convolutional neural network. We use the random kernel in our efficient Fourier convolutional neural network, whose standard deviation of the Gaussian distribution is used as a weight parameter. In other words, since only two scalars for each imaginary and real component per channel are required, a very small number of parameters is applied compressively. Therefore, as a result of experimenting in shallow networks, such as LeNet-3 and LeNet-5, our method achieves competitive accuracy with conventional convolutional neural networks while dramatically reducing the number of parameters. Furthermore, our proposed Fourier network, using a basic three-by-three kernel, mostly performs with higher accuracy than traditional convolutional neural networks in shallow and deep neural networks. Our experiments represent that presented kernel methods have the potential to be applied in all architecture based on convolutional neural networks.

scholarly journals Steganalisis Blind dengan Metode Convolutional Neural Network (CNN) Yedroudj- Net terhadap Tools Steganografi

2020 ◽  
Vol 7 (4) ◽  
pp. 787
Author(s):  
Nurmi Hidayasari ◽  
Imam Riadi ◽  
Yudi Prayudi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Error Probability ◽  
Learning Performance ◽  
Net Method ◽  
Blind Steganalysis ◽  
Steganalysis Method

<p>Steganalisis digunakan untuk mendeteksi ada atau tidaknya file steganografi. Salah satu kategori steganalisis adalah blind steganalisis, yaitu cara untuk mendeteksi file rahasia tanpa mengetahui metode steganografi apa yang digunakan. Sebuah penelitian mengusulkan bahwa metode Convolutional Neural Networks (CNN) dapat mendeteksi file steganografi menggunakan metode terbaru dengan nilai probabilitas kesalahan rendah dibandingkan metode lain, yaitu CNN Yedroudj-net. Sebagai metode steganalisis Machine Learning terbaru, diperlukan eksperimen untuk mengetahui apakah Yedroudj-net dapat menjadi steganalisis untuk keluaran dari tools steganografi yang biasa digunakan. Mengetahui kinerja CNN Yedroudj-net sangat penting, untuk mengukur tingkat kemampuannya dalam hal steganalisis dari beberapa tools. Apalagi sejauh ini, kinerja Machine Learning masih diragukan dalam blind steganalisis. Ditambah beberapa penelitian sebelumnya hanya berfokus pada metode tertentu untuk membuktikan kinerja teknik yang diusulkan, termasuk Yedroudj-net. Penelitian ini akan menggunakan lima alat yang cukup baik dalam hal steganografi, yaitu Hide In Picture (HIP), OpenStego, SilentEye, Steg dan S-Tools, yang tidak diketahui secara pasti metode steganografi apa yang digunakan pada alat tersebut. Metode Yedroudj-net akan diimplementasikan dalam file steganografi dari output lima alat. Kemudian perbandingan dengan tools steganalisis lain, yaitu StegSpy. Hasil penelitian menunjukkan bahwa Yedroudj-net bisa mendeteksi keberadaan file steganografi. Namun, jika dibandingkan dengan StegSpy hasil gambar yang tidak terdeteksi lebih tinggi.</p><p><em><strong><br /></strong></em></p><p><em><strong>Abstract</strong></em></p><p><em>Steganalysis is used to detect the presence or absence of steganograpy files. One category of steganalysis is blind steganalysis, which is a way to detect secret files without knowing what steganography method is used. A study proposes that the Convolutional Neural Networks (CNN) method can detect steganographic files using the latest method with a low error probability value compared to other methods, namely CNN Yedroudj-net. As the latest Machine Learning steganalysis method, an experiment is needed to find out whether Yedroudj-net can be a steganalysis for the output of commonly used steganography tools. Knowing the performance of CNN Yedroudj-net is very important, to measure the level of ability in terms of steganalysis from several tools. Especially so far, Machine Learning performance is still doubtful in blind steganalysis. Plus some previous research only focused on certain methods to prove the performance of the proposed technique, including Yedroudj-net. This research will use five tools that are good enough in terms of steganography, namely Hide In Picture (HIP), OpenStego, SilentEye, Steg and S-Tools, which is not known exactly what steganography methods are used on the tool. The Yedroudj-net method will be implemented in a steganographic file from the output of five tools. Then compare with other steganalysis tools, namely StegSpy. The results showed that Yedroudj-net could detect the presence of steganographic files. However, when compared with StegSpy the results of undetected images are higher.</em></p>

Content-adaptive convolutional neural network post-processing filter

2021 ◽  
Author(s):  
Maria Santamaria ◽  
Yat-Hong Lam ◽  
Francesco Cricri ◽  
Jani Lainema ◽  
Ramin G. Youvalari ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Post Processing ◽  
Adaptive Convolutional Neural Network ◽  
Content Adaptive

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network
Sign in / Sign up

Export Citation Format

Share Document