A Principal Component Analysis-Based Approach for Single Morphing Attack Detection

Dargaud, Laurine; Ibsen, Mathias; Tapia, Juan; Busch, Christoph

doi:10.1109/wacvw58289.2023.00075

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Principal Component Analysis (PCA): This method involves transforming the data into a new set of uncorrelated variables, called principal components, which capture the most significant information in the data. The importance of features is determined by the amount of variance explained by each principal component Dargaud et al, 2023;Serrão et al, 2023).…”

Section: Related Workmentioning

confidence: 99%

Explaining Xgboost Predictions With Shap Value: A Comprehensive Guide to Interpreting Decision Tree-Based Models

Ergün

2023

New Trends in Computer Sciences

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Understanding the factors that affect Key Performance Indicators (KPIs) and how they affect them is frequently important in sectors where data and data science are crucial. Machine learning is utilized to model and predict pertinent KPIs in order to do this. Interpretability is important, nevertheless, in order to fully comprehend how the model generates its predictions. It enables users to pinpoint which traits have aided the model’s ability to learn and comprehend the data. A practical approach for evaluating the contribution of input attributes to model learning has evolved in the form of SHAP (SHapley Additive exPlanations offer an index for evaluating the influence of each feature on the forecasts made by the model. In this paper, it is demonstrated that the contribution of features to model learning may be precisely estimated when utilizing SHAP values with decision tree-based models, which are frequently used to represent tabular data.

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Section: Related Workmentioning

confidence: 99%

Explaining Xgboost Predictions With Shap Value: A Comprehensive Guide to Interpreting Decision Tree-Based Models

Ergün

2023

New Trends in Computer Sciences

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“…Although developing efficient 3d image morphing increased the resulted image quality, it recently caused a critical security issue, especially if an efficient morphing attack is used, as explained in [44] the authors presented a novel morphing attack, aiming to improve the image visual fidelity, the authors also measured the effectiveness of morphing attack detector, demonstrating that their method is difficult to detect. Thus, detecting forged images became a very important field of study in [45] the authors proposed a technique for detecting a single morphing image attack, using patterns and analyzing the principal component, which produced a good result. Good image segmentation algorithms may also be useful to classify the object or spaces in the image as explained in [46], that can help to detect forged image.…”

Section: 9-3d Face Image Morphingmentioning

confidence: 99%

Image Morphing Techniques: A Review

Aloraibi¹

2023

Technium

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Nowadays image morphing has become one of the important techniques in applications that require a graphical representation of objects. Morphing tools have become very well known among users who work on multimedia applications such as art effects, virtual games, photo morphing, and social media, in addition to scientific and academic fields. There are many algorithms to apply morphing operations, including the basic and improved techniques, which share some essential stages, but vary in the algorithm details and the produced image qualities. Morphing techniques, in general, are based on image features and changing them through a warping process to produce another image or mixing two images to produce a new combined image. This paper provides an overview of different morphing techniques explaining how they work and discuss their features in some terms such as the morph visual quality, technical efficiency, and complexity, which can assist the researcher in the image morphing field to compare and identify morphing techniques that suit their working area.

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