An Empirical Study on the Classification of Chinese News Articles by Machine Learning and Deep Learning Techniques

Huang, Chuen-Min; Jiang, Yijun

doi:10.1109/icmlc48188.2019.8949309

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Bachman et al [42] proposed a contrastive presentation learning approach by maximizing mutual information between features from multiple views of data. Huang et al [44] presented a contrastive learning method that discovers sample-based neighborhoods to facilitate feature representation, which emphasizes the importance of discriminative feature extraction during training, Zhuang et al [45] introduced a contrastive idea that trains embedding functions using a metric of local aggregation, allowing similar data instances to cluster while separating dissimilar ones. These ideas of contrastive loss emphasize the significance of capturing meaningful discriminative representations from data.…”

Section: Contrastive Lossmentioning

confidence: 99%

IR-Capsule: Two-Stream Network for Face Forgery Detection

Lin

Han

et al. 2022

Cogn Comput

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The rapid advancement of deepfake technologies raises significant concerns about the security of face recognition systems. While existing methods leverage the clues left by deepfake techniques for face forgery detection, malicious users may intentionally manipulate forged faces to obscure the traces of deepfake clues and thereby deceive detection tools. Meanwhile, attaining cross-domain robustness for data-based methods poses a challenge due to potential gaps in the training data, which may not encompass samples from all relevant domains. Therefore, in this paper, we introduce a solutiona Cross-Domain Robust Bias Expansion Network (BENet) -designed to enhance face forgery detection. BENet employs an auto-encoder to reconstruct input faces, maintaining the invariance of real faces while selectively enhancing the difference between reconstructed fake faces and their original counterparts. This enhanced bias forms a robust foundation upon which dependable forgery detection can be built. To optimize the reconstruction results in BENet, we employ a bias expansion loss infused with contrastive concepts to attain the aforementioned objective. In addition, to further heighten the amplification of forged clues, BENet incorporates a Latent-Space Attention (LSA) module. This LSA module effectively captures variances in latent features between the auto-encoder's encoder and decoder, placing emphasis on inconsistent forgery-related information. Furthermore, BENet incorporates a crossdomain detector with a threshold to determine whether the sample belongs to a known distribution. The correction of classification results through the cross-domain detector enables BENet to defend against unknown deepfake attacks from cross-domain.Extensive experiments demonstrate the superiority of BENet compared with state-ofthe-art methods in intra-database and cross-database evaluations.

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Section: Contrastive Lossmentioning

confidence: 99%