The Internet of things (IoT) enables mobile devices to connect and exchange information with others over the Internet with a lot of applications in consumer, commercial, and industrial products. With the rapid development of machine learning, IoT with image recognition capability is a new research area to assist mobile devices with processing image information. In this research, we propose the rotation-invariant multi-scale convolutional neural network (RIMS-CNN) to recognize rotated objects, which are commonly seen in real situations. Based on the dihedral group D4 transformations, the RIMS-CNN equips a CNN with multiple rotated tensors and its processing network. Furthermore, multi-scale features and shared weights are employed in the RIMS-CNN to increase performance. Compared with the data augmentation approach of using rotated images at random angles for training, our proposed method can learn inherent convolution kernels for rotational features. Experiments were conducted on the benchmark datasets: MNIST, FASHION-MNIST, CIFAR-10, and CIFAR-100. Significant improvements over the other models were achieved to show that rotational invariance could be learned.

With the surge in online information, unverified claims and fake news, especially in politics and economics, have become rampant. This rise makes distinguishing true from false news crucial, as fake news can manipulate public perception and cause socio-economic harm. Previous research indicates that human-written fake news often employs propaganda techniques to seem credible. However, the smaller volume of fake news data compared to real news leads to imbalances in training datasets. This imbalance can cause models to learn incorrect correlations instead of accurately identifying the semantic features of deceptive content. This paper introduces a counterpropaganda semantic learning framework for detecting fake news. Utilizing existing fake news datasets, we automatically generate propaganda-related summaries, reasons, and scores using a large language model. We then create counterpropaganda semantics by enhancing the potential propaganda elements in news content. The model learns the relationship between news and propaganda through a multi-task learning strategy. Experimental results demonstrate that this framework improves small language models’ accuracy and semantic understanding in detecting fake news while reducing the need for intermediary pre-training and manual review of generated training data. Our model, data, and code are open-source at https://github.com/Ryan-Hu-AI/MTCPSL