CNN-Assisted Steganography -- Integrating Machine Learning with Established Steganographic Techniques

• URL: http://arxiv.org/abs/2304.12503v1
• Date: Tue, 25 Apr 2023 00:19:23 GMT
• Title: CNN-Assisted Steganography -- Integrating Machine Learning with Established Steganographic Techniques
• Authors: Andrew Havard, Theodore Manikas, Eric C. Larson, Mitchell A. Thornton
• Abstract summary: We propose a method to improve steganography by increasing the resilience of stego-media to discovery through steganalysis. Our approach enhances a class of steganographic approaches through the inclusion of a steganographic assistant convolutional neural network (SA-CNN) Our results show that such steganalyzers are less effective when SA-CNN is employed during the generation of a stego-image.
• Score: 5.0468312081378475
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a method to improve steganography by increasing the resilience of stego-media to discovery through steganalysis. Our approach enhances a class of steganographic approaches through the inclusion of a steganographic assistant convolutional neural network (SA-CNN). Previous research showed success in discovering the presence of hidden information within stego-images using trained neural networks as steganalyzers that are applied to stego-images. Our results show that such steganalyzers are less effective when SA-CNN is employed during the generation of a stego-image. We also explore the advantages and disadvantages of representing all the possible outputs of our SA-CNN within a smaller, discrete space, rather than a continuous space. Our SA-CNN enables certain classes of parametric steganographic algorithms to be customized based on characteristics of the cover media in which information is to be embedded. Thus, SA-CNN is adaptive in the sense that it enables the core steganographic algorithm to be especially configured for each particular instance of cover media. Experimental results are provided that employ a recent steganographic technique, S-UNIWARD, both with and without the use of SA-CNN. We then apply both sets of stego-images, those produced with and without SA-CNN, to an exmaple steganalyzer, Yedroudj-Net, and we compare the results. We believe that this approach for the integration of neural networks with hand-crafted algorithms increases the reliability and adaptability of steganographic algorithms.

Related papers

• Cover-separable Fixed Neural Network Steganography via Deep Generative Models [37.08937194546323]
  We propose a Cover-separable Fixed Neural Network Steganography, namely Cs-FNNS. In Cs-FNNS, we propose a Steganographic Perturbation Search (SPS) algorithm to directly encode the secret data into an imperceptible perturbation. We demonstrate the superior performance of the proposed method in terms of visual quality and undetectability.
  arXiv  Detail & Related papers  (2024-07-16T05:47:06Z)
• Advanced Image Segmentation Techniques for Neural Activity Detection via C-fos Immediate Early Gene Expression [0.0]
  We develop a novel workflow for the segmentation process involving Convolutional Neural Networks (CNNs) and the Unet model. We demonstrate the effectiveness of our method in distinguishing areas with significant C-fos expression from normal tissue areas.
  arXiv  Detail & Related papers  (2023-12-13T14:36:16Z)
• Steganography of Steganographic Networks [23.85364443400414]
  Steganography is a technique for covert communication between two parties. We propose a novel scheme for steganography of steganographic networks in this paper.
  arXiv  Detail & Related papers  (2023-02-28T12:27:34Z)
• JPEG Steganalysis Based on Steganographic Feature Enhancement and Graph Attention Learning [15.652077779677091]
  We introduce a novel representation learning algorithm for JPEG steganalysis. The graph attention learning module is designed to avoid global feature loss caused by the local feature learning of convolutional neural network. The feature enhancement module is applied to prevent the stacking of convolutional layers from weakening the steganographic information.
  arXiv  Detail & Related papers  (2023-02-05T01:42:19Z)
• Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks [6.123324869194195]
  Unsupervised image segmentation is an important task in many real-world scenarios where labelled data is of scarce availability. We propose a novel approach that harnesses recent advances in unsupervised learning using a combination of Mutual Information Maximization (MIM), Neural Superpixel and Graph Neural Networks (GNNs) in an end-to-end manner.
  arXiv  Detail & Related papers  (2022-10-21T08:35:18Z)
• Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction [62.691996239590125]
  We propose an adaptive convolutional dictionary network (ACDNet) for metal artifact reduction. Our ACDNet can automatically learn the prior for artifact-free CT images via training data and adaptively adjust the representation kernels for each input CT image. Our method inherits the clear interpretability of model-based methods and maintains the powerful representation ability of learning-based methods.
  arXiv  Detail & Related papers  (2022-05-16T06:49:36Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty [91.0564497403256]
  We present a novel framework that involves probabilistic fusion between the two families of predictions during network training. Our network features a self-attention graph neural network, which drives the learning by enforcing strong interactions between different correspondences. We propose motion parmeterizations suitable for learning and show that our method achieves state-of-the-art performance on the challenging DeMoN and ScanNet datasets.
  arXiv  Detail & Related papers  (2021-04-16T17:59:06Z)
• Knowledge Distillation By Sparse Representation Matching [107.87219371697063]
  We propose Sparse Representation Matching (SRM) to transfer intermediate knowledge from one Convolutional Network (CNN) to another by utilizing sparse representation. We formulate as a neural processing block, which can be efficiently optimized using gradient descent and integrated into any CNN in a plug-and-play manner. Our experiments demonstrate that is robust to architectural differences between the teacher and student networks, and outperforms other KD techniques across several datasets.
  arXiv  Detail & Related papers  (2021-03-31T11:47:47Z)
• Video-based Facial Expression Recognition using Graph Convolutional Networks [57.980827038988735]
  We introduce a Graph Convolutional Network (GCN) layer into a common CNN-RNN based model for video-based facial expression recognition. We evaluate our method on three widely-used datasets, CK+, Oulu-CASIA and MMI, and also one challenging wild dataset AFEW8.0.
  arXiv  Detail & Related papers  (2020-10-26T07:31:51Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.