Color Image Steganography using Dual Wavelet Transforms

Abbasi, Mahdi

doi:10.5120/ijca2019918639

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Although IWT has some competitive properties, such as fast computation and efficient memory usages, the IWT technique is seen to have more applications in multimedia fields of data compression with lossless recovery and data hiding with high security. Recently, IWT has also been further integrated with DWT for image fusion in the application of image steganography constructions with data concealments in multimedia communication [ 28 ]. The feasibility of IWT used in the field of pattern recognition with multimodality fusion and deep learning may be evaluated in future work.…”

Section: Discussionmentioning

confidence: 99%

CNN Deep Learning with Wavelet Image Fusion of CCD RGB-IR and Depth-Grayscale Sensor Data for Hand Gesture Intention Recognition

Ding

Zheng

2022

Sensors

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Pixel-based images captured by a charge-coupled device (CCD) with infrared (IR) LEDs around the image sensor are the well-known CCD Red–Green–Blue IR (the so-called CCD RGB-IR) data. The CCD RGB-IR data are generally acquired for video surveillance applications. Currently, CCD RGB-IR information has been further used to perform human gesture recognition on surveillance. Gesture recognition, including hand gesture intention recognition, is attracting great attention in the field of deep neural network (DNN) calculations. For further enhancing conventional CCD RGB-IR gesture recognition by DNN, this work proposes a deep learning framework for gesture recognition where a convolution neural network (CNN) incorporated with wavelet image fusion of CCD RGB-IR and additional depth-based depth-grayscale images (captured from depth sensors of the famous Microsoft Kinect device) is constructed for gesture intention recognition. In the proposed CNN with wavelet image fusion, a five-level discrete wavelet transformation (DWT) with three different wavelet decomposition merge strategies, namely, max-min, min-max and mean-mean, is employed; the visual geometry group (VGG)-16 CNN is used for deep learning and recognition of the wavelet fused gesture images. Experiments on the classifications of ten hand gesture intention actions (specified in a scenario of laboratory interactions) show that by additionally incorporating depth-grayscale data into CCD RGB-IR gesture recognition one will be able to further increase the averaged recognition accuracy to 83.88% for the VGG-16 CNN with min-max wavelet image fusion of the CCD RGB-IR and depth-grayscale data, which is obviously superior to the 75.33% of VGG-16 CNN with only CCD RGB-IR.

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Section: Discussionmentioning

confidence: 99%

CNN Deep Learning with Wavelet Image Fusion of CCD RGB-IR and Depth-Grayscale Sensor Data for Hand Gesture Intention Recognition

Ding

Zheng

2022

Sensors

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“…The first metric is Mean Squared Error (MSE) which is used to measure the difference between two images. This metric is obtained using the definition mentioned in equation (3). A small value of the MSE metric means high similarity level between the stego-image and original image.…”

Section: Verification Measurements (A) Evaluation Metrics Of the Covementioning

confidence: 99%

“…The second is the capacity of secret information which is measured in bits/bytes/samples. The third is security which measures the percent of difficulty to detect the secret information; also it can be used to obtain the quality of the secured signal [3].…”

Section: Introductionmentioning

confidence: 99%

Hiding Multi Short Audio Signals in Color Image by using Fast Fourier Transform

Jamel

2021

ANJS

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Many purposes require communicating audio files between the users using different applications of social media. The security level of these applications is limited; at the same time many audio files are secured and must be accessed by authorized persons only, while, most present works attempt to hide single audio file in certain cover media. In this paper, a new approach of hiding three audio signals with unequal sizes in single color digital image has been proposed using the frequencies transform of this image. In the proposed approach, the Fast Fourier Transform was adopted where each audio signal is embedded in specific region with high frequencies in the frequency spectrum of the cover image to save much more details of the cover image and avoid any doubts that there is any secret information are hidden inside it. The quality of the stego-image and the extracted audio files are evaluated with the standard evaluation metric. The simulation results shown significant results of these metrics and achieve good imperceptibility and high security of the stego-image. The SNR and SPCC values are considered acceptance that means significant in terms quality and similarity of the reconstructed signal.

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Color Image Steganography using Dual Wavelet Transforms

Cited by 2 publications

References 15 publications

CNN Deep Learning with Wavelet Image Fusion of CCD RGB-IR and Depth-Grayscale Sensor Data for Hand Gesture Intention Recognition

CNN Deep Learning with Wavelet Image Fusion of CCD RGB-IR and Depth-Grayscale Sensor Data for Hand Gesture Intention Recognition

Hiding Multi Short Audio Signals in Color Image by using Fast Fourier Transform

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