computer-vision-10

Steganalysis

3260 papers • 126 benchmarks • 313 datasets

Detect the usage of Steganography

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Most implemented papers

SteganoGAN: High Capacity Image Steganography with GANs

K. Veeramachaneni, Alfredo Cuesta-Infante, K. Zhang, Lei Xu•Fri Jan 11 2019

This paper proposes a novel technique for hiding arbitrary binary data in images using generative adversarial networks which allow us to optimize the perceptual quality of the images produced by the model.

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Learning Rich Features for Image Manipulation Detection

Xintong Han, L. Davis, Peng Zhou, Vlad I. Morariu•Sat May 12 2018

A two-stream Faster R-CNN network is proposed and trained end-to-end to detect the tampered regions given a manipulated image and fuse features from the two streams through a bilinear pooling layer to further incorporate spatial co-occurrence of these two modalities.

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Deep Residual Network for Steganalysis of Digital Images

M. Boroumand, Mo Chen, J. Fridrich•Tue Apr 30 2019

Steganography detectors built as deep convolutional neural networks have firmly established themselves as superior to the previous detection paradigm – classifiers based on rich media models. Existing network architectures, however, still contain elements designed by hand, such as fixed or constrained convolutional kernels, heuristic initialization of kernels, the thresholded linear unit that mimics truncation in rich models, quantization of feature maps, and awareness of JPEG phase. In this work, we describe a deep residual architecture designed to minimize the use of heuristics and externally enforced elements that is universal in the sense that it provides state-of-the-art detection accuracy for both spatial-domain and JPEG steganography. The key part of the proposed architecture is a significantly expanded front part of the detector that “computes noise residuals” in which pooling has been disabled to prevent suppression of the stego signal. Extensive experiments show the superior performance of this network with a significant improvement, especially in the JPEG domain. Further performance boost is observed by supplying the selection channel as a second channel.

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GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection

R. Vera-Rodríguez, Julian Fierrez, Rubén Tolosana, João C. Neves, Vasco Lopes, Hugo Proencca•Tue Nov 12 2019

The results obtained in the empirical evaluation show that additional efforts are required to develop robust facial manipulation detection systems against unseen conditions and spoof techniques, such as the one proposed in this study.

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Empty Cities: A Dynamic-Object-Invariant Space for Visual SLAM

César Cadena, Berta Bescós, José Neira•Wed Oct 14 2020

An end-to-end deep learning framework to turn images of an urban environment that include dynamic content, such as vehicles or pedestrians, into realistic static frames suitable for localization and mapping is introduced.

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Steganographic generative adversarial networks

Evgeny Burnaev, Denis Volkhonskiy, I. Nazarov, B. Borisenko•Wed Mar 15 2017

A new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN) is proposed, which allows to generate more setganalysis-secure message embedding using standard steganography algorithms.

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RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network

Yongfeng Huang, Zinan Lin, Jilong Wang•Wed Feb 14 2018

Quantization index modulation (QIM) steganography makes it possible to hide secret information in voice-over IP (VoIP) streams, which could be utilized by unauthorized entities to set up covert channels for malicious purposes. Detecting short QIM steganography samples, as is required by real circumstances, remains an unsolved challenge. In this paper, we propose an effective online steganalysis method to detect QIM steganography. We find four strong codeword correlation patterns in VoIP streams, which will be distorted after embedding with hidden data. To extract those correlation features, we propose the codeword correlation model, which is based on recurrent neural network (RNN). Furthermore, we propose the feature classification model to classify those correlation features into cover speech and stego speech categories. The whole RNN-based steganalysis model (RNN-SM) is trained in a supervised learning framework. Experiments show that on full embedding rate samples, RNN-SM is of high detection accuracy, which remains over 90% even when the sample is as short as 0.1 s, and is significantly higher than other state-of-the-art methods. For the challenging task of conducting steganalysis towards low embedding rate samples, RNN-SM also achieves a high accuracy. The average testing time for each sample is below 0.15% of sample length. These clues show that RNN-SM meets the short sample detection demand and is a state-of-the-art algorithm for online VoIP steganalysis.

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