A novel CNN based security guaranteed image watermarking generation scenario for smart city applications

Li, Daming; Deng, Lizhen; Gupta, Brij B.; Wang, Haoxiang; Choi, Chang

doi:10.1016/j.ins.2018.02.060

Cited by 281 publications

(112 citation statements)

References 13 publications

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“…Recently, many studies investigated CNNs for various applications such as security [28,29], medical [30], and action recognition [4,5]. In this article, motivated from the aforementioned studies we use CNN for target detection.…”

Section: A) Fine-tuning Yolo Object Detection Modelmentioning

confidence: 99%

Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM

Hussain

Muhammad

Ullah

et al. 2020

IEEE Trans. Ind. Inf.

112

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The massive amount of video data produced by surveillance networks in industries instigate various challenges in exploring these videos for many applications such as video summarization (VS), analysis, indexing, and retrieval. The task of multi-view video summarization (MVS) is very challenging due to the gigantic size of data, redundancy, overlapping in views, light variations, and inter-view correlations. To address these challenges, various low-level features and clustering based soft computing techniques are proposed that cannot fully exploit MVS. In this article, we achieve MVS by integrating deep neural network based soft computing techniques in a two tier framework. The first online tier performs target appearance based shots segmentation and stores them in a lookup table that is transmitted to cloud for further processing. The second tier extracts deep features from each frame of a sequence in the lookup table and pass them to deep bi-directional long short-term memory (DB-LSTM) to acquire probabilities of informativeness and generate a summary. Experimental 1 evaluation on benchmark dataset and industrial surveillance data from YouTube confirms the better performance of our system compared to state-of-the-art MVS.

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Section: A) Fine-tuning Yolo Object Detection Modelmentioning

confidence: 99%

Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM

Hussain

Muhammad

Ullah

et al. 2020

IEEE Trans. Ind. Inf.

112

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show abstract

“…2 illustrates the overall architecture of the proposed scheme with some example images. Given two input spaces that are all the possible inputs of watermark images and cover-images (W and C, respectively), neural network µ θ 1 parameterized by θ 1 is applied to learn a function that encodes W. W f , the encoded space of W, not only enlarges Kandi et al [8] No No Robust to common image processing attacks Multi-bit Vukotic et al [9] Learning extraction Yes Robust to Rotation, JPEG, and Cropping Single-bit Li et al [10] No No No Multi-bit Fierro-Radilla et al [11] No No Robust to common image processing attacks Zero-watermarking Kim et al [18] Assisting extraction No Focus on geometric attacks Template-based watermarking Mun et al [19] Learning W to prepare for the next-step concatenation, but also brings some redundancy, decomposition, and perceivable randomness to help information protection and robustness. Like the embedding process in traditional watermarking where an encoded w is inserted into a feature space of c, in the proposed scheme, an embedder that takes W f , C as inputs and produces the marked-image is fit by the neural network σ θ 2 parameterized by θ 2 .…”

Section: A the Overview Architecture Of The Proposed Schemementioning

confidence: 99%

“…Recent research on image watermarking tasks with deep neural networks has emerged [9], [10], [11], [12], but there still exist challenging issues. For example, it is difficult to fully utilize the fitting ability of deep neural networks to automatically learn and generalize both the watermark embedding and extracting processes.…”

mentioning

confidence: 99%

An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

Zhong

Huang

Mastorakis

et al. 2021

IEEE Trans. Multimedia

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Digital image watermarking is the process of embedding and extracting a watermark covertly on a cover-image. To dynamically adapt image watermarking algorithms, deep learning-based image watermarking schemes have attracted increased attention during recent years. However, existing deep learning-based watermarking methods neither fully apply the fitting ability to learn and automate the embedding and extracting algorithms, nor achieve the properties of robustness and blindness simultaneously. In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed. To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm. A deep learning architecture is specially designed for image watermarking tasks, which will be trained in an unsupervised manner to avoid human intervention and annotation. To facilitate flexible applications, the robustness of the proposed scheme is achieved without requiring any prior knowledge or adversarial examples of possible attacks. A challenging case of watermark extraction from phone camera-captured images demonstrates the robustness and practicality of the proposal. The experiments, evaluation, and application cases confirm the superiority of the proposed scheme.

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“…In addition, in the aspect of deep neural network model protection, Uchida et al [Uchida, Nagai, Sakazawa et al (2017)] propose to embed the digital watermark into the trained neural network model to achieve the purpose of copyright protection. Li et al [Li, Deng, Gupta et al (2018)] propose a security-guaranteed image watermarking generation scenario for city applications based on CNN. Rouhani et al [Rouhani, Chen and Koushanfar (2018)] propose deepsigns.…”

Section: Watermarking Algorithms Based On Deep Learningmentioning

confidence: 99%

A Survey of Image Information Hiding Algorithms Based on Deep Learning

Meng¹,

Cui²,

Yuan³

2018

CMES

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With the development of data science and technology, information security has been further concerned. In order to solve privacy problems such as personal privacy being peeped and copyright being infringed, information hiding algorithms has been developed. Image information hiding is to make use of the redundancy of the cover image to hide secret information in it. Ensuring that the stego image cannot be distinguished from the cover image, and sending secret information to receiver through the transmission of the stego image. At present, the model based on deep learning is also widely applied to the field of information hiding. This paper makes an overall conclusion on image information hiding based on deep learning. It is divided into four parts of steganography algorithms, watermarking embedding algorithms, coverless information hiding algorithms and steganalysis algorithms based on deep learning. From these four aspects, the state-of-the-art information hiding technologies based on deep learning are illustrated and analyzed.

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A novel CNN based security guaranteed image watermarking generation scenario for smart city applications

Cited by 281 publications

References 13 publications

Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM

Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM

An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

A Survey of Image Information Hiding Algorithms Based on Deep Learning

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