Titlebook: Adversarial Multimedia Forensics; Ehsan Nowroozi,Kassem Kallas,Alireza Jolfaei Book 2024 The Editor(s) (if applicable) and The Author(s),

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H. T. Banks,S. C. Beeler,H. T. Trandeep learning architectures (DL),Synthetic satellite images offer a wide range of potential applications, including the generation of massive, labeled datasets for artificial intelligence (AI) applications, manipulated image detection, and natural disaster monitoring and detection. Although deep lea

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M. Bergounioux,T. Männikkö,D. Tiba counter technology to linguistic steganography, aims at revealing the existence of additional data within unknown texts. Early linguistic steganography algorithms alter a text carrier to embed additional data, which limits the embedding payload due to the extremely small number of changeable operat

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https://doi.org/10.1007/978-3-540-71119-3addition to the improvement in the robustness of forensic detectors to targeted attacks mentioned in Chen et al. (IEEE Trans Inf Forensics Secur 14(9):2454–2469, 2019). This paper specifically investigates the transferability of adversarial cases targeting the original Convolutional Neural Network (

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https://doi.org/10.1007/978-3-540-71119-3 become crucial to combat these challenges. In this work, we propose a novel approach for detecting synthetic human voices by leveraging the identification of artifacts generated by neural vocoders in audio signals. Neural vocoders are specialized neural networks synthesizing waveforms using tempora

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Adversarial Multimedia Forensics978-3-031-49803-9Series ISSN 1568-2633 Series E-ISSN 2512-2193

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