Reversible Audio Data Hiding in Spectral and Time Domains

2013 ◽  
pp. 19-41 ◽  
Author(s):  
Akira Nishimura
Keyword(s):  
Data Hiding ◽  
Time Domain ◽  
Reversible Data Hiding ◽  
Linear Prediction ◽  
Data Consistency ◽  
Time Domains ◽  
Audio Data ◽  
Hidden Data ◽  
The Time Domain ◽  
Location Map

Reversible data hiding is a technique whereby hidden data are embedded in host data in such a way that the host data consistency is perfectly preserved and the host data are restored when extracting the hidden data. This chapter introduces basic algorithms for reversible data hiding, histogram shifting, histogram expansion, and compression. This chapter also proposes and evaluates two reversible data hiding methods, i.e., hiding data in the frequency-domain using integer Discrete Cosine Transform (DCT) and modified DCT and hiding in the time domain using linear prediction and error expansion. As no location map is required to prevent amplitude overflow, the proposed method in the time domain achieves a storage capacity of nearly 1 bit per sample of payload data. The proposed methods are evaluated by the payload amount, objective quality degradation of stego signal, and payload concealment.

scholarly journals Separable Reversible Data Hiding in Encrypted Images Based on Difference Histogram Modification

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Dawen Xu ◽  
Shubing Su
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Linear Prediction ◽  
Data Extraction ◽  
Histogram Modification ◽  
Modulo Operation ◽  
Before And After ◽  
Hidden Data ◽  
Encrypted Images ◽  
Difference Histogram

In this paper, an efficient reversible data hiding method for encrypted image based on neighborhood prediction is proposed, which includes image encryption, reversible data hiding in encrypted domain, and hidden data extraction. The cover image is first partitioned into non-overlapping blocks, and then the pixel value in each block is encrypted by modulo operation. Therefore, the linear prediction difference in the block that satisfies the specific condition is consistent before and after encryption, ensuring that data extraction is completely separable from image decryption. In addition, by using the linear weighting of three adjacent pixels in the block to predict the current pixel, the prediction accuracy can be improved. The data-hider, who does not know the original image content, may embed additional data based on prediction difference histogram modification. Data extraction and image recovery are free of any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme.

scholarly journals A Location Map Reduced Reversible Data Hiding Method Using Prediction Error Modification

2012 ◽  
Vol 6-7 ◽  
pp. 428-433
Author(s):  
Yan Wei Li ◽  
Mei Chen Wu ◽  
Tung Shou Chen ◽  
Wien Hong
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Experimental Results ◽  
Cover Image ◽  
Prediction Errors ◽  
Secret Data ◽  
Location Map ◽  
Comparable Image Quality

We propose a reversible data hiding technique to improve Hong and Chen’s (2010) method. Hong and Chen divide the cover image into pixel group, and use reference pixels to predict other pixel values. Data are then embedded by modifying the prediction errors. However, when solving the overflow and underflow problems, they employ a location map to record the position of saturated pixels, and these pixels will not be used to carry data. In their method, if the image has a plenty of saturated pixels, the payload is decreased significantly because a lot of saturated pixels will not joint the embedment. We improve Hong and Chen’s method such that the saturated pixels can be used to carry data. The positions of these saturated pixels are then recorded in a location map, and the location map is embedded together with the secret data. The experimental results illustrate that the proposed method has better payload, will providing a comparable image quality.

scholarly journals Reversible Data Hiding Using Inter-Component Prediction in Multiview Video Plus Depth

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 514 ◽  
Author(s):  
Jin Young Lee ◽  
Cheonshik Kim ◽  
Ching-Nung Yang
Keyword(s):  
Video Compression ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Depth Map ◽  
3D Video ◽  
Internet Technology ◽  
Secret Communication ◽  
Multiview Video ◽  
Prediction Error Expansion ◽  
Hidden Data

With the advent of 3D video compression and Internet technology, 3D videos have been deployed worldwide. Data hiding is a part of watermarking technologies and has many capabilities. In this paper, we use 3D video as a cover medium for secret communication using a reversible data hiding (RDH) technology. RDH is advantageous, because the cover image can be completely recovered after extraction of the hidden data. Recently, Chung et al. introduced RDH for depth map using prediction-error expansion (PEE) and rhombus prediction for marking of 3D videos. The performance of Chung et al.’s method is efficient, but they did not find the way for developing pixel resources to maximize data capacity. In this paper, we will improve the performance of embedding capacity using PEE, inter-component prediction, and allowable pixel ranges. Inter-component prediction utilizes a strong correlation between the texture image and the depth map in MVD. Moreover, our proposed scheme provides an ability to control the quality of depth map by a simple formula. Experimental results demonstrate that the proposed method is more efficient than the existing RDH methods in terms of capacity.

scholarly journals A Joint Encryption and Reversible Data Hiding Scheme Based on Integer-DWT and Arnold Map Permutation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Shun Zhang ◽  
Tiegang Gao ◽  
Guorui Sheng
Keyword(s):  
Frequency Domain ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Cover Image ◽  
Discrete Wavelet ◽  
Original Image ◽  
Histogram Modification ◽  
Encrypted Image ◽  
Hidden Data ◽  
Arnold Map

A joint encryption and reversible data hiding (joint encryption-RDH) scheme is proposed in this paper. The cover image is transformed to the frequency domain with integer discrete wavelet transform (integer DWT) for the encryption and data hiding. Additional data is hidden into the permuted middle (LH, HL) and high (HH) frequency subbands of integer DWT coefficients with a histogram modification based method. A combination of permutations both in the frequency domain and in the spatial domain is imposed for the encryption. In the receiving end, the encrypted image with hidden data can be decrypted to the image with hidden data, which is similar to the original image without hidden data, by only using the encryption key; if someone has both the data hiding key and the encryption key, he can both extract the hidden data and reversibly recover the original image. Experimental results demonstrate that, compared with existing joint encryption-RDH schemes, the proposed scheme has gained larger embedding capacity, and the distribution of the encrypted image with data hidden has a random like behavior. It can also achieve the lossless restoration of the cover image.

scholarly journals Tone Recognition Database of Electronic Pipe Organ Based on Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shuyi Zhao
Keyword(s):  
Artificial Intelligence ◽  
Fourier Transform ◽  
Human Computer Interaction ◽  
Time Domain ◽  
Pipe Organ ◽  
Interaction Interface ◽  
Tone Recognition ◽  
Audio Data ◽  
The Time Domain ◽  
Domain Information

In the past few decades, artificial intelligence technology has experienced rapid development, and its application in modern industrial systems has grown rapidly. This research mainly discusses the construction of a database of electronic pipe organ tone recognition based on artificial intelligence. The timbre synthesis module realizes the timbre synthesis of the electronic pipe organ according to the current timbre parameters. The audio time domain information (that is, the audio data obtained by file analysis) is framed and windowed, and fast Fourier transform (FFT) is performed on each frame to obtain the frequency domain information of each frame. The harmonic peak method based on improved confidence is used to identify the pitch, obtain the fundamental tone of the tone, and calculate its multiplier. Based on the timbre parameters obtained in the timbre parameter editing interface, calculate the frequency domain information of the synthesized timbre of each frame, and then perform the inverse Fourier transform to obtain the time domain waveform of each frame; connect the time domain waveforms of different frames by the cross-average method to obtain the time-domain waveform of the synthesized tone (that is, the audio data of the synthesized tone). After collecting the sound of the electronic pipe organ, the audio needs to be denoised, and the imported audio file needs to be parsed to obtain the audio data information. Then, the audio data are frequency-converted and the timbre characteristic information is analyzed; the timbre parameters are obtained through the human-computer interaction interface based on artificial intelligence, and the timbre of the electronic pipe organ is generated. If the timbre effect is not satisfactory, you can re-edit the timbre parameters through the human-computer interaction interface to generate timbre. During the experiment, the overall recognition rate of 3762 notes and 286 beats was 88.6%. The model designed in this study can flexibly generate electronic pipe organ sound libraries of different qualities to meet the requirements of sound authenticity.

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