A Daily Tourism Demand Prediction Framework Based on Multi-head Attention CNN: The Case of The Foreign Entrant in South Korea

Developing an accurate tourism forecasting model is essential for making desirable policy decisions for tourism management. Early studies on tourism management focus on discovering external factors related to tourism demand. Recent studies utilize deep learning in demand forecasting along with these external factors. They mainly use recursive neural network models such as LSTM and RNN for their frameworks. However, these models are not suitable for use in forecasting tourism demand. This is because tourism demand is strongly affected by changes in various external factors, and recursive neural network models have limitations in handling these multivariate inputs. We propose a multi-head attention CNN model (MHAC) for addressing these limitations. The MHAC uses 1D-convolutional neural network to analyze temporal patterns and the attention mechanism to reflect correlations between input variables. This model makes it possible to extract spatiotemporal characteristics from time-series data of various variables. We apply our forecasting framework to predict inbound tourist changes in South Korea by considering external factors such as politics, disease, season, and attraction of Korean culture. The performance results of extensive experiments show that our method outperforms other deep-learning-based prediction frameworks in South Korea tourism forecasting.

Facial Manipulation Detection Based on the Color Distribution Analysis in Edge Region

In this work, we present a generalized and robust facial manipulation detection method based on color distribution analysis of the vertical region of edge in a manipulated image. Most of the contemporary facial manipulation method involves pixel correction procedures for reducing awkwardness of pixel value differences along the facial boundary in a synthesized image. For this procedure, there are distinctive differences in the facial boundary between face manipulated image and unforged natural image. Also, in the forged image, there should be distinctive and unnatural features in the gap distribution between facial boundary and background edge region because it tends to damage the natural effect of lighting. We design the neural network for detecting face-manipulated image with these distinctive features in facial boundary and background edge. Our extensive experiments show that our method outperforms other existing face manipulation detection methods on detecting synthesized face image in various datasets regardless of whether it has participated in training.