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Article summary:
1. This paper proposes a fine-grained pornographic image recognition approach using multiple feature fusion transfer learning.
2. Traditional methods for recognizing pornographic images are based on hand-crafted features, such as skin color and texture detection.
3. Deep convolutional neural networks (CNNs) have been used to recognize pornographic images with high accuracy and robustness.
Article analysis:
The article is generally reliable and trustworthy, as it provides a comprehensive overview of the current state of research in the field of pornographic image recognition, including both traditional hand-crafted feature-based methods and more recent deep learning approaches. The authors provide detailed descriptions of the datasets used in their experiments, as well as the network architecture employed for their proposed method. Furthermore, they provide evidence for their claims by citing relevant research papers throughout the article.
However, there are some potential biases that should be noted. For example, the authors focus mainly on deep learning approaches to recognizing pornographic images, while not providing an equal amount of detail about traditional hand-crafted feature-based methods. Additionally, while they do mention potential risks associated with viewing online pornography (such as reduced working effectiveness and higher divorce rates), they do not explore any possible counterarguments or alternative perspectives on this issue. Finally, there is no discussion of any promotional content or partiality in the article; however, it should be noted that this could be a potential source of bias if present in other sources cited by the authors.