The Pixogram: Addressing High Payload Demands for Video Steganography

Rabie, Tamer; Baziyad, Mohammed

doi:10.1109/access.2019.2898838

Cited by 28 publications

(8 citation statements)

References 36 publications

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“…(3) Just as an example if we consider that 1 character takes n sec audio and cover video is of length l and video is of m frames then the stego video can be represented as Where V(l) represents video in ith frame and A(l) represents audio in ith frame the merging function which is used to overlap audio with video. If we want to extract any character in middle that is if we want to get the ith character in kth frame, then it can be got by the equation The total number of characters that can be embedded in a video of length l is given by (6)…”

Section: Algorithm To Embed Audio To Videomentioning

confidence: 99%

Hiding of Text Tunes in Video

Sushma¹,

Manjula²

2020

IJETAE

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Recent advancement in digital usage have led to fast access to data and has also made sharing of sensitive information possible. Any how these advancements come with a price where any personal or sensitive information is at stake. Now a days, it is exceptionally dangerous to deal with information on web against attackers. There are various methods which secure the sensitive data against breaches. The main concern is to send the secret data across an insecure communication channel. There are variety of ways to secure sensitive data. Few of them to name are cryptography, steganography, watermarking etc. In this paper a novel method where a secret text is converted to audio with different tunes and merged with video is proposed. Since the audio will be played along with video the possibility of identifying the existence of secret data is less. Experimental results have shown that the time taken to send the secret audio along with video is the same as sending original video.

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Section: Algorithm To Embed Audio To Videomentioning

confidence: 99%

Hiding of Text Tunes in Video

Sushma¹,

Manjula²

2020

IJETAE

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“…spatial domain methods directly hide the confidential message into the cover carrier. Least Significant Bit (LSB) techniques are the most popular techniques of spatial domain methods due to their low computational complexity and simplicity [8], [22]- [24]. LSB techniques operate by substituting the confidential message bits with some LSBs of pixels from the video carrier frames.…”

Section: Introductionmentioning

confidence: 99%

“…These techniques attempt to take advantage of the 3D nature of videos and exploit the third dimension, which is the time dimension in embedding. This extra dimension provides some additional features, such as motion components and motion vectors [26] [24]. This paper proposes a new approach for hiding information within digital videos, which is based mainly on two well-known algorithms, namely Shi-Tomasi corner detector algorithm and Four Least Significant Bits (4-LSBs) algorithm.…”

Section: Introductionmentioning

confidence: 99%

A New Video Steganography Scheme Based on Shi-Tomasi Corner Detector

et al. 2020

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Recent developments in the speed of the Internet and information technology have made the rapid exchange of multimedia information possible. However, these developments in technology lead to violations of information security and private information. Digital steganography provides the ability to protect private information that has become essential in the current Internet age. Among all digital media, digital video has become of interest to many researchers due to its high capacity for hiding sensitive data. Numerous video steganography methods have recently been proposed to prevent secret data from being stolen. Nevertheless, these methods have multiple issues related to visual imperceptibly, robustness, and embedding capacity. To tackle these issues, this paper proposes a new approach to video steganography based on the corner point principle and LSBs algorithm. The proposed method first uses Shi-Tomasi algorithm to detect regions of corner points within the cover video frames. Then, it uses 4-LSBs algorithm to hide confidential data inside the identified corner points. Besides, before the embedding process, the proposed method encrypts confidential data using Arnold's cat map method to boost the security level. Experimental results revealed that the proposed method is highly secure and highly invisible, in addition to its satisfactory robustness against Salt & Pepper noise, Speckle noise, and Gaussian noise attacks, which has an average Structural Similarity Index (SSIM) of more than 0.81. Moreover, the results showed that the proposed method outperforms state-of-the-art methods in terms of visual imperceptibility, which offers excellent peak signalto-noise ratio (PSNR) of average 60.7 dB, maintaining excellent embedding capacity.

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“…Steganography is a technique of covert communication by hiding information into digital media (such as image [1], video [2], audio [3] and text [4]) without causing any…”

Section: Introductionmentioning

confidence: 99%

Detecting Steganography in Inactive Voice-Over-IP Frames Based on Statistic Characteristics of Fundamental Frequency

Tian¹,

Liu²,

Chang

et al. 2020

IEEE Access

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Steganography in inactive Voice-over-IP frames is a new technique of information hiding, which can achieve large steganographic capacity while maintaining excellent imperceptibility. To prevent the illegitimate use of this technique, the entropy-based and poker test-based steganalysis methods have been presented. However, the detection performance of these two methods is not so good for the cases of having small quantity of inactive frames or low embedding rates. Thus, we present a new steganalysis method based on statistic characteristics of fundamental frequency. Specifically, we employ the statistics for zero-crossing count (ZCC), including the average ZCC of inactive frames, the ratio between the average ZCC of inactive frames and that of all frames, and the difference between the average ZCC of inactive frames and their calibrated versions, to characterize the frame-level dynamic characteristic of speech signals; we utilize the average values of Mel-frequency cepstral coefficients (MFCCs) to represent the invariant characteristic of inactive frames; further, using the feature set consisting of the zero-crossing statistics and average MFCCs, we propose a support-vector-machine based steganalysis for inactive speech frames. The proposed steganalysis method is evaluated with a large number of ITU-T G.723.1 encoded speech samples, and compared with the existing methods. The experimental results demonstrate that the proposed method significantly outperforms the previous ones on detection accuracy, false positive rate and false negative rate for any given embedding rates or using the same number of inactive frames. Particularly, the proposed method can provide accurate detecting results for the existing steganographic methods only using very small quantity of inactive frames, and thereby be employed to detecting potential inactive-frame steganography behaviors in real-time speech streams.

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The Pixogram: Addressing High Payload Demands for Video Steganography

Cited by 28 publications

References 36 publications

Hiding of Text Tunes in Video

Hiding of Text Tunes in Video

A New Video Steganography Scheme Based on Shi-Tomasi Corner Detector

Detecting Steganography in Inactive Voice-Over-IP Frames Based on Statistic Characteristics of Fundamental Frequency

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