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Steganography is the art and science of hiding the information in the carrier object. The main objective of steganography is to hide the existence of data. Lots of advancements has been done in the use of carrier medium, started from image, sound, video and network packets and protocols. Information hiding inside image, audio and video is often called digital steganography whereas if network packets and/or protocols are used then it is called network steganography. Using this technique we can send small amount of information confidentially. Here in this paper, various approaches towards network steganography and current status of research in this field are discussed.

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<p>Satellite images captured by the sensor do not contain the reflectance values returned from the earth due to absorption and scattering caused by the atmospheric elements. Atmospheric correction (AC) retrieves the actual reflectance values, called surface reflectance (SR). Existing models are based on physics-based radiative transfer codes that rely on precomputed lookup tables, require many atmospheric parameters, and involve high computation costs. A few of these parameters are difficult to calculate and must be estimated. Deep learning models can be an excellent approximator to such physics-based AC models. In the proposed paper, we design, develop and analyze a deep learning end-to-end model. It is trained with a seasonally and spatially rich dataset to perform AC of Landsat 8 satellite images without explicitly considering atmospheric parameters. Experiments on model predictions are carried out to validate the effectiveness using Landsat data and ground measurements provided by RadCalNet. The results are encouraging because the model predicts SR values with good accuracy and establishes a high correlation with the reference data.</p>