Qualification: 
M.E
p_malathy@cb.amrita.edu

Malathi P. currently serves as Assistant Professor (Sr. Grade) at the Department of Computer Science and Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore Campus. Her areas of research include Image Steganography, DNA steganography, Image Processing and Machine Learning.

Publications

Publication Type: Conference Paper

Year of Publication Title

2020

M. Chanchal, Malathi P., and Dr. Gireesh K. T., “A Comprehensive Survey on Neural Network based Image Data Hiding Scheme”, in 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2020.[Abstract]


The advancement in science and technology has become a significant bottleneck to the information/data. In order to overcome the threats, a number of information hiding methodologies have been proposed. Among such techniques, the most commonly used technique is information hiding in an image due to the presence of secret information in an image, where it will be hard to be identified by the attacker. The appearance of both the normal image and the image that contains the secret information will be similar. Also, with a number of deep learning and AI techniques, data hiding can be done in an increasingly better way. This paper summarizes various neural network-based image steganography techniques and also the methodology used in each paper. Also, this research paper discusses the advantages and drawbacks faced by each technique.

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Publication Type: Journal Article

Year of Publication Title

2020

P. C. Nikkil Kumar and Malathi P., “A Survey on Various Shadow Detection and Removal Methods”, Advances in Intelligent Systems and Computing, pp. 395-401, 2020.[Abstract]


Shadows plays an inevitable part in an image and also the major source of hindrance in Computer Vision analysis. Shadow detection is the performance enhancement process that increases the accuracy of the Computer Vision algorithms like Object Tracking, Object Recognition, Image Segmentation, Surveillance, Scene Analysis, Stereo, Tracking, etc. Shadows limit the stability of these algorithms, and hence detecting shadows and its elimination are profound pre-processing techniques for improving execution of Vision algorithms efficiently. To label the challenges under various environmental conditions, researches have been carried out to develop various algorithms and techniques for shadow detection. This paper objective is to bring comparative analysis of shadow detection techniques with their pros and cons.

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2020

Malathi P., M, A. Sridhar, Paliwal, A., and Dr. Gireesh K. T., “Maximizing the Embedding Efficiency Using Linear Block Codes in Spatial and Transform Domains”, Procedia Computer Science, vol. 167, pp. 302-312, 2020.[Abstract]


Steganographic schemes are used in many fields, as it provides high-security transmission of messages. The strength of the steganographic scheme depends on embedding efficiency. Embedding efficiency is understood as the average number of random data bits that must be embedded in an object per one embedding change. The below-done work introduces an efficient embedding technique using various linear block codes useful to both spatial and frequency domain of digital images. Linear block coding algorithms like binary hamming code, Random linear code, Cyclic code, Reed Solomon code, etc., are applied to embed restricted data inside the image, which improves embedding efficiency. Higher embedding efficiency translates to better steganographic security. The Mean squared error (MSE), Peak Signal to noise ratio (PSNR), Structural Similarity Index (SSIM), Normalized Absolute Error (NAE) and Correlation Coefficient values for each algorithm is calculated and compared. Histogram attack is one of the steganalysis techniques which is used in this paper to confirm the security provided to the restricted data. This comparison shows that Binary Hamming code in the spatial domain and transform domain provides better results as compared to the other coding theory algorithms.

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2018

R. E. Vinodhini and Malathi P., “DNA Based Image Steganography”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 819-829, 2018.[Abstract]


Providing security to all kinds of data is an essential task in the world of digital data. The data can be secured using the several methods like cryptography, steganography and watermarking. Steganography hides the data behind the cover object to secure it. To increase the security of the data, dual cover objects can be used. In this paper, image and DNA are the two covers, which are used to secure the data. The DNA insertion algorithm is used to hide the data in the DNA sequence and it results a fake DNA sequence. The capacity, payload, BPN and Cracking Probability are calculated for the fake DNA sequence to ensure the security. The fake DNA is hidden in a cover image using LSB and F5 algorithm. The MSE and PSNR values are calculated and compared. To conform the security to the data, steganalysis method called histogram attack is performed and compared. This works shows that F5 Algorithm is better compared to LSB algorithm in the second layer of hiding the data.

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2016

Malathi P., D. Bharathi, and Vinodhini, R. E., “Illumination Invariant Face Recognition using Fisher Linear Discriminant Algorithm (FLDA)”, International Journal of Control Theory and Applications, vol. 9, no. 10, pp. 4201-4210, 2016.[Abstract]


In image processing domain biometrics is an emerging field, in which matching the face images of optical and infrared is a tough toil. Since the optical and infrared images are captured by two disparate devices there exists a great diversity between one and the other kinds of images. A classy method supported by Feature discriminant analysis[1], which uses fisher linear discriminant algorithm (FLDA) is proposed in this paper. This approach has two steps to minimize this chaos and to maximize the performance of optical-infrared face recognition. In first step, extract all the common discriminant features from heterogeneous (infrared and optical) face images using FLDA. In second step, k-Nearest Neighbors (k-NN) algorithm is used on the result to conclude whether they match or not. To show that the algorithm works better than the existing ones, experiments are conducted on optical and infrared datasets.

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2015

Malathi P., Ajith, V., and Kanagaraj, S., “Image Steganography Based on LSB Matching Revisited using Secret Sharing Application”, International Journal of Applied Engineering Research, vol. 10, no. 55, pp. 2931-2938, 2015.[Abstract]


Least significant bit (LSB) method is a well-known steganographic algorithm in the spatial domain. LSB drops the visual quality of the image and leads to poor security. To overcome this the least significant bit matching revisited steganography was expanded and developed an edge adaptive image steganography. To provide more security for this scheme, we combine this scheme with (u, v) secret image sharing algorithm. With additional facility of steganography plus authentication and proposes a new algorithm in this paper, which is more secure compared to the previous scheme.

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2015

Malathi P. and Meera, M., “An Improved Embedding Scheme in Compressed Domain Image Steganography”, International Journal of Applied Engineering Research, vol. 10, no. 55, pp. 1933-1937, 2015.[Abstract]


Compressed domain embedding techniques has proven to be resistant against many steganalysis techniques, yet the capacity of the medium is a constraint. The number of non-zero quantized coefficients will be less than the actual number of DCT coefficients, which reduces the capacity of the cover image. This paper proposes a method to embed the message using an extension to hamming code, which was proven to improve the embedding capacity. It also compares the results with existing techniques.

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Publication Type: Conference Proceedings

Year of Publication Title

2018

R. E. Vinodhini, Malathi P., and T. Kumar, G., “An Improved Approach for Securing Document Images Using Dual Cover”, Advances in Signal Processing and Intelligent Recognition Systems, vol. 678. Springer International Publishing, Cham, pp. 155-166, 2018.[Abstract]


Security is essential to all varieties of data that is transmitted through an open network or the internet. Steganography is such a technique which provides security for any kind of data by hiding it inside a cover object. The images with confidential information like medical reports, passport, academic certificates, Aadhar card and agreements between governments need to be secured while transferring through the internet. In this paper, dual cover steganography is used to provide a better security which prevents the confidential data from attackers. This approach uses two layers of covers, image, and DNA. Using improved DNA insertion method the document image is hidden inside a DNA sequence. The BPN, capacity, payload and embedding time are calculated for the DNA sequence. The improved insertion method is used in this paper since it gives the very low cracking probability. The fake DNA is hidden inside a cover image using matrix embedding with hamming code algorithm for both spatial and transform domain of an image. The Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Maximum difference, average difference, structural content are calculated and compared. To ensure the security of document image the statistical attack called histogram analysis attack is performed and compared. This comparison shows that the matrix embedding with hamming code in the frequency domain provides better security for the second layer of hiding the data.

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2017

R. E. Vinodhini, Malathi P., and Dr. Gireesh K. T., “A Survey on DNA and Image Steganography”, 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, Coimbatore, India, 2017.[Abstract]


Steganography is the classical technique of securing the confidential data. The data is hidden in an object called cover medium. The encoder (sender) and the decoder (receiver) only have the knowledge of secret data hidden in the cover object. An image is a cover object that hides the secret data. DNA is another cover object which has a huge data storage capacity and it hides a very large secret data by replacing the bases which are of huge numbers to a particular DNA. In this study, the various techniques that are applied for hiding a secret message in different cover objects like image and DNA are summarized and the integrity of the secret message in various mediums are discussed. The combination of DNA and Images used together to hide a secret message or data is known as dual cover steganography; here the cover objects are DNA and Image. This work also provides all the existing methods on Image steganography, DNA-based steganography and Dual Cover steganography, their merits, and demerits.

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2017

Malathi P., Manoaj, M., Manoj, R., Raghavan, V., and Vinodhini, R. E., “Highly Improved DNA Based Steganography”, Procedia Computer Science, vol. 115. Elsevier B.V., pp. 651-659, 2017.[Abstract]


Steganography facilitates to conceal the confidential information within mediums like image, video, audio, DNA, etc. In this paper, the DNA steganography is developed by using improved DNA insertion algorithm for improving the security to the information. In this paper the modification of the DNA insertion algorithm is used because of its low cracking probability. The confidential information like secret messages and document images are hidden inside the DNA sequence and the performance is measured by calculating the cracking probability, BPN, payload and capacity.

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2016

Malathi P. and Dr. Gireesh K. T., “Relating the Embedding Efficiency of LSB Steganography Techniques in Spatial and Transform Domains”, Procedia Computer Science, vol. 93. Elsevier, Rajagiri School of Engineering and TechnologyKochi; India, pp. 878-885, 2016.[Abstract]


This paper is based on image steganography that is Least Significant Bits (LSB) techniques on images to enhance the security of the communication. The LSB-based technique is the most challenging one because it is difficult to differentiate between the cover-object and stego-object, if few LSB bits of the cover object are replaced. The LSB approach combined with F5 algorithm and matrix embedding which is applied on both spatial and frequency domain of an image. The Mean squared error (MSE) and Peak signal to noise ratio (PSNR) was used as the performance measure to compare the different LSB techniques. This paper shows that MSE and PSNR of LSB techniques with Matrix embedding yields better results.

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