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Author

K. Pradeep Reddy

Bio: K. Pradeep Reddy is an academic researcher from K L University. The author has contributed to research in topics: Steganography & Machine learning. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Papers
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01 Jan 2015
TL;DR: This paper is discussing how to protect the steganography image by embedding it into another medium using mat lab using image matrices, and enhances the security to a higher level.
Abstract: Steganography helps in communication of secured data in several carries like images, videos and audio. It undergoes many useful applications and well known for ill intentions. It was mainly proposed for the security techniques in the increase of computational power, in order to have security awareness like individuals, groups, agencies etc. The factors that are separated from cryptography and water making are data is not detectable; capacity of hidden data is unknown and robustness of medium. The steganography provides different methods existing and guidelines. The current technology of image steganography involves techniques of LSB in image domain but once the attacker acknowledges that medium is containing embedded data he will attack the medium and breaks into the secured content. In this paper we are discussing how to protect the steganography image by embedding it into another medium using mat lab. Here we work on image matrices to perform the steganography. Lightness adjustment on the matrix is done to reduce the brighter pixels in image. The lightness decreased image then embedded into another cover image by matrix difference technique (will be discussed in detail). This enhances the security to a higher level because to acquire the steganography image embedded we need to have the key image which will be having only by the receiver. And from millions of images on the internet it is impossible for an attacker to guess the key image. And this enhances the level of security.

3 citations

Journal ArticleDOI
TL;DR: This work worked on the remote driven vehicle in a more secured method using the Mel Frequency cepstral Coefficient, which is very rapid and more reliable for the speech detection thus the remote driver can use the MFCC.
Abstract: Speech recognition is a rapidly emerging technology in Human Computer Interaction HCI. It has many applications as we use it from search engines to the device control it serves many areas we interact every day from dawn to dusk. Along with the uses we have many limitations in speech processing such as Language barrier, Accent and Noise, so to implement the speech processing we have many challenges. To enable the advantages of this speech processing most of the leading software companies like Apple, Microsoft and Google are continuously evolving their speech enabled applications. The speech processing eases the physically challenged people’s interaction with the devices and makes them productive. The Idea of the automatically driven cars is introduced by Google and Audi, but they are not acceptable in most of the cases because of lacking trust in current technology. Thus we here worked on the remote driven vehicle in a more secured method using the Mel Frequency cepstral Coefficient. The secure driving of the vehicle can be ensured by the remote driver. This technique is very rapid and more reliable for the speech detection thus the remote driver can use the MFCC and the video from the vehicle needed to be broadcasted to the remote driver that can be done an IP camera running on a data network. And the instructions can from the remote driver can be sent to the vehicle by an app created with python that connected to a micro controller. To minimize the limitations in the remote drive the vehicle must be enabled with automatic braking when obstacle approaches which can be done by the Ultrasonic sensors that do the distance estimation. The remote drivers usually have a very limited view of road they drive and they must get the precise edges of the road this can be achieved by processing the stream of images to calculate the edge in Matlab.

Cited by
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Journal ArticleDOI
TL;DR: Improvement of the steganography techniques by hiding the required message into the suitable frequency band is presented, and results show that the increase of the message length will reduce the Peak Signal to Noise Ratio (PSNR), while the PSNR increases with the increasing the DWT levels.
Abstract: Recently the Discrete-Wavelet-Transform (DWT) has been represented as signal processing powerful tool to separate the signal into its band frequency components. In this paper, improvement of the steganography techniques by hiding the required message into the suitable frequency band is presented. The results show that the increase of the message length will reduce the Peak Signal to Noise Ratio (PSNR), while the PSNR increases with the increasing the DWT levels. It should be noted that the PSNR reduction was from -13.8278 to -17.77208 when increasing the message length from 161 to 505 characters. In this context, the PSNR is increased from -13.8278 to 7.0554 and from -17.7208 to 1.7901 when the DWT increased from level (1) to level (2).

8 citations

Proceedings ArticleDOI
01 Feb 2020
TL;DR: Following work has been done using bit steganography in which bits of one image have been used which is called cover image to hide message image and results have been done on the basis of mean square error ad peak signal to noise ratio.
Abstract: Hidden communication is important since ancient times to provide the secrecy of information which may be for personal work or for professional work. In the ancient times cryptography was the main method for secret communication. In cryptography message is encrypted in some way but the existence of message is known to the third party which gives the possibility of hacking of the message by third party easily. Here comes the use of steganography which overcomes the drawback of cryptography. In steganography the existence of message is not known to the third party that makes it easy for two parties to communicate in an imperceptible manner. There are various techniques using steganography. Following work has been done using bit steganography in which bits of one image have been used which is called cover image to hide message image. This technique has been used on every bit of each pixel of cover image starting from LSB to MSB and then comparison of results has been done on the basis of mean square error ad peak signal to noise ratio.

2 citations

Journal ArticleDOI
TL;DR: Experiments have been performed with the proposed and existing technique and result proves that the efficiency of the proposed method is better in terms of security.
Abstract: Image Steganography is the technology that is being used to provide secure communication between the sender and the receiver. As the technology enhances, security becomes the important concern. Thus, image Steganography helps in embedding the data behind the image with the original image so that unauthorized user cannot access the data. In this paper, different techniques of image Steganography are discussed. This paper also concludes the comparison of the proposed technique with the existing techniques. Encoding of the data will reduce the risk of data tampering by unauthorized user and increase the security of the system. So the image format conversion is also used that will provide additional security to the data. Experiments have been performed with the proposed and existing technique and result proves that the efficiency of the proposed method is better in terms of security.
Proceedings ArticleDOI
02 Feb 2023
TL;DR: In this paper , an automated arrhythmia classification using Harris Hawks Optimization-based DL (AC-HHODL) technique in an IoT environment is presented, where the MobileNetv2 model was executed for producing a collection of feature vectors.
Abstract: An electrocardiogram (ECG) has been widely utilized for evaluating heart disease which is effect more explores cardiac problems and recognition of cardiovascular diseases. The procedure is easy, fast, and non-invasive. But, artificially identifying and classifying heart disease is difficult, as manually examining the ECG signals is time-consuming. In recent years, a huge growth in the control of IoT can witness that gives a lot in the health care method as it allows continuous monitoring of patients but there is a requirement for advanced automatic monitoring methods for classifier of cardiac arrests. This article develops an automated arrhythmia classification using Harris Hawks Optimization-based DL (AC-HHODL) technique in an IoT environment. The AC-HHODL technique focuses on the identification and categorization of arrhythmia using ECG signals in the IoT environment. In the AC-HHODL technique, the MobileNetv2 model was executed for producing a collection of feature vectors. In addition, the HHO technique was exploited for the optimum hyperparameter adjustment of the MobileNetv2 model. Furthermore, least square support vector machine (LS -SVM) approach is applied for accurate arrhythmia classification. To validate the better performance of the AC-HHODL approach, the experiments were carried out on arrhythmia dataset. On the other hand, with respect to sensy, the AC-HHODL algorithm has managed to report increased sensy of 98.65% while the ResNet, ResNet-attention, DenseNet, VGG-16, and XGBoost approaches have exposed degraded sensy of 91.67%, 89.47%, 95.34%, 95.77% and 90.40% respectively. The simulation output inferred the improvised efficacy of the AC-HHODL technique over other models.