This paper proposes a novel method for tamper detection and recovery using semi-fragile data hiding, based on Lifting Wavelet Transform (LWT) and Feed-Forward Neural Network (FNN). In TRLF, first, the host image is decomposed up to one level using LWT, and the Discrete Cosine Transform (DCT) is applied to each 2×2 blocks of diagonal details. Next, a random binary sequence is embedded in each block as the watermark by correlating DC coefficients. In authentication stage, first, the watermarked image geometry is reconstructed by using Speeded Up Robust Features (SURF) algorithm and extract watermark bits by using FNN. Afterward, logical exclusive-or operation between original and extracted watermark is applied to detect tampered region. Eventually, in the recovery stage, tampered regions are recovered by image digest which is generated by inverse halftoning technique. The performance and efficiency of TRLF and its robustness against various geometric, nongeometric and hybrid attacks are reported. From the experimental results, it can be seen that TRLF is superior in terms of robustness and quality of the digest and watermarked image respectively, compared to the-state-of-the-art fragile and semifragile watermarking methods. In addition, imperceptibility has been improved by using different correlation steps as the gain factor for flat (smooth) and texture (rough) blocks.
In the last decades, the area under cultivation of maize products has increased because of its essential role in the food cycle for humans, livestock, and poultry. Moreover, the diseases of plants impact food safety and can significantly reduce both the quality and quantity of agricultural products. There are many challenges to accurate and timely diagnosis of the disease. This research presents a novel scheme based on a deep neural network to overcome the mentioned challenges. Due to the limited number of data, the transfer learning technique is employed with the help of two well-known architectures. In this way, a new effective model is adopted by a combination of pre-trained MobileNetV2 and Inception Networks due to their effective performance on object detection problems. The convolution layers of MoblieNetV2 and Inception modules are parallelly arranged as earlier layers to extract crucial features. In addition, the imbalance problem of classes has been solved by an augmentation strategy. The proposed scheme has a superior performance compared to other state-of-the-art models published in recent years. The accuracy of the model reaches 97%, approximately. In summary, experimental results prove the method's validity and significant performance in diagnosing disease in plant leaves.
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