Artificial Neural Networks Applied to Image Steganography

Brandão, Alexandre Santos; Jorge, David Calhau

doi:10.1109/tla.2016.7459621

Cited by 30 publications

(13 citation statements)

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“…Syndrom-trellis codes are special codes used to reduce the embedding distortion results from adding data to the original image. However, machine learning based approaches such as Artificial Neural Network (ANN) [43] can detect the existence of embedded data. Machine learning based models such as rich models [44] and deep learning models [45,49].…”

Section: B3 Ai Based Attacksmentioning

confidence: 99%

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Attacking Image Watermarking and Steganography - A Survey

Hosam¹

2019

IJITCS

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Image hiding techniques include steganography and watermarking. Steganography procedures are directed to keep the secure information from eavesdropping and perturbations. On the other hand, watermarking algorithms are used for keeping the watermark robust to attacks. When the attacker tries to perturb the carrier image to remove the watermark, the image quality will be degraded to level that makes it useless. Data hiding is essential in many applications such as communication channel security, data security and forgery detection. Watermarking is used in copyright protection. Image hiding attacks can be active or passive. In active attack, the attacker changes the content of the data. While in passive attacks the attacker tries to guess the secure data by eavesdropping. In this paper, we discuss the image data hiding attacks that directed to both secure message and carrier image. First, message attacks such as Oracle and Template attacks will be discussed. Second, the carrier image attacks are presented in two broad categories, namely passive and active attacks. Finally, the paper conclusion will be presented. The paper presented image data hiding attack types in professional and well-organized categories.

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Section: B3 Ai Based Attacksmentioning

confidence: 99%

“…In steganography systems, the secure data is not related to the carrier image. The cover image is used as a channel for covert communication [3]. For example, terrorists may use internet images as a communication media.…”

Section: Introductionmentioning

confidence: 99%

Attacking Image Watermarking and Steganography - A Survey

Hosam¹

2019

IJITCS

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“…The presence of hidden image in the audio signal can be detected by steganalysis techniques and the same is used to extract the hidden data from it. In [7], artificial neural network algorithm is used to detect and extract the hidden information from the image. The estimate of secure payload that the image can handle and the level of secret data detected from the image is calculated in [8].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Security Enhancement in Image Transmission Using Steganography

Saravanan¹,

Priya²

2019

JIEC

149

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Data communication through the public communication channels is insecure due to advanced technology available for interception of third party users. Therefore, an efficient data hiding technology is vital for secure data transmission especially when the system is connected with internet services. Audio steganography is one of the promising solutions for where the information is hidden over audio file. In this paper, a novel method for the hiding of image information by converting into another format thereby reduces the computational complexity.

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“…The process of features optimization used swarm-based optimization algorithms [9,10,21]. The swarm-based algorithms are multi-objective and multi constraint-based fitness function and generate a better optimal solution instead of unguided algorithms for the optimization of features used particle swarm optimization and ant colony optimization [13][14][15][16][17][18]. The particle and ant colony optimization both are used features optimization for better selection and generation of patterns.…”

Section: Introductionmentioning

confidence: 99%

Optimized Features of SIFT Transform Function for Digital Image Watermarking using Hybrid Swarm Intelligence and Neural Network

Kumar¹,

Sharma²

2019

IJRTE

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Authentication of digital multi-media is a challenging task in the current scenario of internet technology for the authentication of digital multi-media data used digital watermarking techniques. The feature-based watermarking techniques provide the robustness of digital watermarking methods. In this paper proposed optimized features based digital watermarking techniques using a neural network. For the process of features, optimization used hybrid swarm intelligence algorithms. The hybrid algorithms are a combination of PSO and ACO. The PSO used as feature optimizer and ACO used for the selection of features point for the processing of neural network models. Two neural networks models used BP and RBF. The property of both models is different for the processing of data and desired output for the enhancement of the proposed model used cascaded neural network models using BP and RBF. The cascaded models generate dynamic patterns for the embedding of a digital watermark. The dynamic patterns provide the randomness of the pixel value and decrease the value of attack predication. The proposed algorithms have been tested on an extensive database of 300 images. The analysis of the proposed algorithm is satisfactory against different types of attacks and enhance the strength of robustness.

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Artificial Neural Networks Applied to Image Steganography

Cited by 30 publications

References 0 publications

Attacking Image Watermarking and Steganography - A Survey

Attacking Image Watermarking and Steganography - A Survey

An Algorithm for Security Enhancement in Image Transmission Using Steganography

Optimized Features of SIFT Transform Function for Digital Image Watermarking using Hybrid Swarm Intelligence and Neural Network

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