A secured image steganography method based on ballot transform and genetic algorithm

Multimedia Tools and Applications - Rapid advances in digital technology have facilitated us to transfer a huge amount of electronic files over the internet. But in the presence of malicious...     

Image steganography using deep learning based edge detection

This paper introduces a deep learning-based Steganography method for hiding secret information within the cover image. For this, we use a convolutional neural network (CNN) with Deep Supervision based edge detector, which can retain more edge pixels over conventional edge detection algorithms. Initially, the cover image is pre-processed by masking the last 5-bits of each pixel. The said edge detector model is then applied to obtain a gray-scale edge map. To get the prominent edge information, the gray-scale edge map is converted into a binary version using both global and adaptive binarization schemes. The purpose of using different binarization techniques is to prove the less sensitive nature of the edge detection method to the thresholding approaches. Our rule for embedding secret bits within the cover image is as follows: more bits into the edge pixels while fewer bits into the non-edge pixels. Experimental outcomes on various standard images confirm that compared to state-of-the-art methods, the proposed method achieves a higher payload.

     

Design and development of a portable resistive sensor based on α‐MnO2/GQD nanocomposites for trace quantification of Pb(II) in water

The occurrence of heavy metal ions in food chain is appearing to be a major problem for mankind. The traces of heavy metals, especially Pb(II) ions present in water bodies remains undetected, untreated, and it remains in the food cycle causing serious health hazards for human and livestock. The consumption of Pb(II) ions may lead to serious medical complications including multiple organ failure which can be fatal. The conventional methods of heavy metal detection are costly, time‐consuming and require laboratory space. There is an immediate need to develop a cost‐effective and portable sensing system which can easily be used by the common man without any technical knowhow. A portable resistive device with miniaturized electronics is developed with microfluidic well and α‐MnO₂/GQD nanocomposites as a sensing material for the sensitive detection of Pb(II). α‐MnO₂/GQD nanocomposites which can be easily integrated with the miniaturized electronics for real‐time on‐field applications. The proposed sensor exhibited a tremendous potential to be integrated with conventional water purification appliances (household and commercial) to give an indication of safety index for the drinking water. The developed portable sensor required low sample volume (200 µL) and was assessed within the Pb(II) concentration range of 0.001 nM to 1 uM. The Limit of Detection (LoD) and sensitivity was calculated to be 0.81 nM and 1.05 kΩ/nM/mm², and was validated with the commercial impedance analyser. The shelf‐life of the portable sensor was found to be ∼45 days.     

Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms

Neural Computing and Applications - Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided...