Model evaluation is critical in deep learning. However, the traditional model evaluation approach is susceptible to issues of untrustworthiness, including insecure data and model sharing, insecure model training, incorrect model evaluation, centralized model evaluation, and evaluation results that can be tampered easily. To minimize these untrustworthiness issues, this paper proposes a blockchain-based model evaluation framework. The framework consists of an access control layer, a storage layer, a model training layer, and a model evaluation layer. The access control layer facilitates secure resource sharing. To achieve fine-grained and flexible access control, an attribute-based access control model combining the idea of a role-based access control model is adopted. A smart contract is designed to manage the access control policies stored in the blockchain ledger. The storage layer ensures efficient and secure storage of resources. Resource files are stored in the IPFS, with the encrypted results of their index addresses recorded in the blockchain ledger. Another smart contract is designed to achieve decentralized and efficient management of resource records. The model training layer performs training on users’ servers, and, to ensure security, the training data must have records in the blockchain. The model evaluation layer utilizes the recorded data to evaluate the recorded models. A method in the smart contract of the storage layer is designed to enable evaluation, with scores automatically uploaded as a resource attribute. The proposed framework is applied to deep learning-based motion object segmentation, demonstrating its key functionalities. Furthermore, we validated the storage strategy adopted by the framework, and the trustworthiness of the framework is also analyzed.
Abstract-Contrast enhancement operation is often used to highlight some information, weaken or remove some unwanted information in tampered images. In this paper, a tampered image detecting method is proposed based on wavelet analysis for specific fingerprints left by contrast enhancement operation in image. Firstly, the RGB color space of image was converted into YCbCr color space, and the Y monochrome channel image was extracted; secondly, the normalized energy was calculated in the wavelet details sub-bands after wavelet transform of image's pixel value histogram of the component; finally, the tampered image was identified according to the normalized energy. The results of the experiment show there is an obvious distinction between tampered and unaltered sub-blocks divided. Furthermore, the contrast experiment results about wavelet transform and Fourier transform show that the former is better than the latter in both accuracy and time complexity.
When collecting tongue images in an open en- vironment with a mobile portable collection device, there will be problems of different shooting angles and unstable lighting. Due to the strong mobility of the portable acquisition device, the captured images will inevitably be blurred by jitter, which further increases the difficulty of segmentation. This paper applies neural network to tongue images segmentation, and proposes a tongue images segmentation method based on deep convolutional neural network. This method is a tongue images segmentation method based on the semantic segmentation framework of DeeplabV3+. First, we modify the output category of the network. Because only the tongue region is segmented, segmentation targets can be divided into two categories when performing tongue images segmentation. One is the tongue region and the other is the background region. Then we replace the backbone network of DeeplabV3+ with a lightweight network and add an attention mechanism. Finally, we use the collected tongue images in the open environment to train the network. After the network obtains the initial segmentation result, tongue images are restored according to the same type of label, so as to obtain the required tongue images only containing tongues. The experimental results show that the method has higher segmentation accuracy for tongue images in open environment, and can better meet the needs of people for tongue images segmentation.
Abstract-Aiming at the problem that uneven illumination in natural scene image has serious interferences on accurate text location, a new method based on homomorphic filtering and color distribution is presented. The homomorphic filtering is to reduce illumination changes and enhance the edge details. Utilizing the color distribution, the text distribution is prominent and easy to get the rough location. And the prior knowledge is needed to obtain the final text location. Experiments prove the validity and the better flexibility of this method.
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