An Improved DCT Based Steganography Technique - Bansal, Deepika;Chhikara, Rita
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Steganography is the art and science of hiding the existence of the communication, i.e., it hides secret messages inside other file formats. The word was derived from the greek word steganos which means "covered" and graphia means "writing" i.e. covered writing. To human eyes, data usually contain known forms like images, email, sound and text. Most internet data naturally include unwarranted headers too. These are media exploited using steganography techniques. Many methods for hiding data in digital images have been proposed, so far: Some embed secret data in the spatial domain, and others…mehr

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Produktbeschreibung
Steganography is the art and science of hiding the existence of the communication, i.e., it hides secret messages inside other file formats. The word was derived from the greek word steganos which means "covered" and graphia means "writing" i.e. covered writing. To human eyes, data usually contain known forms like images, email, sound and text. Most internet data naturally include unwarranted headers too. These are media exploited using steganography techniques. Many methods for hiding data in digital images have been proposed, so far: Some embed secret data in the spatial domain, and others hide them in the frequency domain. In this project, the proposed work Shield algorithm is compared with six existing steganographic algorithms. A steganographic algorithm for hiding the secret message in the cover images is proposed in authors' work. The quantized DCT coefficients are used for selecting the number of bits to be hidden in the image. The proposed steganographic method can provide a high information embedding capacity and successfully increase the security. The author's demonstration program is coded in Matlab R2010a.
Autorenporträt
Deepika Bansal is working as a Research Associate and Rita Chhikara is working as an Assistant Professor in The NorthCap University, Gurugram. Their current areas of research include Data Mining, Machine Learning, Neural Networks, Image Processing and Deep Learning.