A two-phase gradient based feature embedding approach. (September 2021) - Chatterjee, Agneet; Ghosh, Soulib; Chakraborty, Anuran; Ghosal, Sudipta Kr; Sarkar, Ram
Highlights: OCR is imbibed with spatial domain steganography to make the latter more secure. Character level features of text image are embedded instead of image itself. Quantity of embedded message for each pixel is varied to ensure least distortion. One of the two candidate feature sets is embedded which causes least distortion. As features are of no use without trained OCR model, this makes the method safer. Abstract: Over the years, researchers attempted to maximize the capacity, imperceptibility, and robustness of Steganography algorithms. Nowadays, the challenge is not only to maximize these parameters rather identify new ideas toward the security aspect. Keeping this requirement in mind, in the present scope of the work, we introduce a novel Steganographic approach mingled with optical character recognition (OCR) techniques to maximize the security against intruders who aim to extract the hidden data. We introduce two novel feature descriptors, the modified shadow and percentage of light source (PLS) feature specifically keeping in mind the embedding requirements. We train our model on a standard publicly available dataset, also reasoning the idea behind doing so. In our embedding method, we vary the quantity of the embedded message for each pixel, based on the gradient value of that pixel, to ensure the least distortion which human beings cannot differentiate. Here, the message image (text image) is not embedded in its entirety, rather character level features, extracted from the text message, are then embedded in the cover image. This very phase ensures that even if the intruder has the access to the stego image, s/he cannot extract the data as it is not having the raw data rather some features. Even the feature values alone would not give the notion of a secret image as the intruder requires the trained OCR model, which the receiver has, to recognize the text. Furthermore, for every character image extracted from the text, we choose between two candidate feature sets and embed the one which causes the least distortion. The feature extraction step generates high recognition accuracy, which in turn completes the extraction process. The security model goes on to show good results in terms of the Steganography metrics. We also perform a fusion of standard robustness and statistical tests, whereby our embedded image successfully performs well.