Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection

Akhriev, Albert; Mareček, Jakub

doi:10.1109/ism46123.2019.00045

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…Pimentel et al [18] integrated autoencoders with active learning, enhancing unsupervised anomaly detection models. Akhriev et al [19] combined regular data deep autoencoding with unique thresholding techniques to detect anomalies. The use of autoencoders as a foundational element in BENet architecture for cross-domain robust face forgery detection aligns with their demonstrated effectiveness in anomaly identification and data representation.…”

Section: Autoencodermentioning

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: Autoencodermentioning

confidence: 99%

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

Lin

Han

et al. 2022

Cogn Comput

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“…In the second step, we consider the individual residuals as samples of an empirical distribution, and take the value at risk (VaR) at λ as a threshold. We provide details in (Akhriev and Marecek 2019;Akhriev, Marecek, and Simonetto 2018). The test as to whether residual at each sensor is below the threshold results in a binary map, suggesting whether the observation of each sensor is likely to have come from our model or not.…”

Section: The Overall Schemamentioning

confidence: 99%

Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise

Akhriev¹,

Mareček²,

Simonetto³

2020

AAAI

Self Cite

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A Review of Neural Networks for Anomaly Detection

et al. 2022

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Anomaly detection is a critical issue across several academic fields and real-world applications. Artificial neural networks have been proposed to detect anomalies from different input types, but there is no clear guide to deciding which model to use in a specific case. Therefore, this study examines the most relevant Neural Network Outlier Detection algorithms in the literature, compares their benefits and drawbacks in some application scenarios, and displays their outcomes in benchmark datasets. The initial search revealed 1422 papers on projects completed between 2017 and 2021. These papers were further narrowed based on title, abstract, quality assessment, inclusion, and exclusion criteria, remaining 76 articles. Finally, we reviewed these publications and verified that Autoencoder Neural Network, Convolutional Neural Network, Recurrent Neural Network, and Generative Adversarial Network have promisor outcomes for outlier detection, the advantages of these neural networks for outlier detection, and the significant challenges of outlier detection strategies.

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Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection

Cited by 5 publications

References 37 publications

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

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

Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise

A Review of Neural Networks for Anomaly Detection

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