Globally, fresh vegetables are a crucial part of our lives and they provide most of the vitamins, minerals, and proteins, in short, every nutrition that a growing body need. They vary in colors like; red, green, and yellow but as our ancestors say that green vegetables are a must for every age. To identify the fresh vegetable that makes our body healthy and notion positive the proposed automatic multi-class vegetable classifier is used. In this paper, a framework based on a deep learning approach has been proposed for multi-class vegetable classification from scratch. The accuracy of the proposed model is further increased using the transfer-learning concept (DenseNet201). The whole process is divided into four modules; data collection and pre-processing, data splitting, CNN model training, and testing, and performance improvement using a pre-trained DenseNet201 network. Data augmentation and data shuffling are used to free from lack of data availability during the training phase of the model. The proposed framework is more efficient and can predict the type of vegetables comparatively in less computational time (2 to 3 minutes) with an ‘Accuracy’ of 98.58%, ‘Sensitivity’ of 98.23%, and ‘Specificity’ of 94.25%.

False-positive problem (FPP) is a one of the challenging tasks for the researchers. It authenticates the wrong owner to access the multimedia content. To overcome, the FPP problem, this paper introduces an efficient watermarking method based on the selection of highest entropy blocks. In this method, cover and watermark images are initially shuffled through Arnold transform. Then, the encrypted images are further processed by a 2-level discrete wavelet transform followed by singular value decomposition. The proposed method has been evaluated with geometrical, filtering, noise, and contrast adjustment attacks on the standard image datasets against five recently developed watermarking methods. The simulation results reveal that the proposed method outperforms the existing methods. INDEX TERMSColor watermarking, False positive problem, Arnold transform, Discrete wavelet transform, Singular value decomposition.

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