Image Copy Detection Based on Convolutional Neural Networks

Zhang, Jing; Zhu, Wenting; Li, Bing; Hu, Weiming; Yang, Jinfeng

doi:10.1007/978-981-10-3005-5_10

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Deep networks are only used in straight-forward ways, e.g. training in a Siamese way [65], which we interpret as loss of interest by the research community due to the lack of new, challenging, large benchmarks that reflect same category Real-world imaging process that generates image pairs that are considered similar at different granularity levels. real world applications.…”

Section: Tasks and Methodsmentioning

confidence: 99%

The 2021 Image Similarity Dataset and Challenge

Douze¹,

Tolias²,

Pizzi³

et al. 2021

Preprint

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This paper introduces a new benchmark for largescale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to determine whether a query image is a modified copy of any image in a reference corpus of size 1 million. The benchmark features a variety of image transformations such as automated transformations, hand-crafted image edits and machine-learning based manipulations. This mimics real-life cases appearing in social media, for example for integrity-related problems dealing with misinformation and objectionable content. The strength of the image manipulations, and therefore the difficulty of the benchmark, is calibrated according to the performance of a set of baseline approaches. Both the query and reference set contain a majority of "distractor" images that do not match, which corresponds to a real-life needle-in-haystack setting, and the evaluation metric reflects that. We expect the DISC21 benchmark to promote image copy detection as an important and challenging computer vision task and refresh the state of the art.

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Section: Tasks and Methodsmentioning

confidence: 99%

The 2021 Image Similarity Dataset and Challenge

Douze¹,

Tolias²,

Pizzi³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The blocks were fed into CNN that is recurrent in nature for the forgery detection with SVM as the classifier model. One more CNN model is used in [31] to detect the copy and move image forgery. It uses the Siamese neural network for the forgery detection with 3 convolutional layers, 2 max-pooling layers and 2 fully connected layers.…”

Section: Related Workmentioning

confidence: 99%

Image forgery detection based on fusion of lightweight deep learning models

Doegar¹,

Hiriyannaiah²,

Matt³

et al. 2021

Turk J Elec Eng & Comp Sci

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Image forgery detection is one of the key challenges in various real time applications, social media and online information platforms. The conventional methods of detection based on the traces of image manipulations are limited to the scope of predefined assumptions like hand-crafted features, size and contrast. In this paper, we propose a fusion based decision approach for image forgery detection. The fusion of decision is based on the lightweight deep learning models namely SqueezeNet, MobileNetV2 and ShuffleNet. The fusion decision system is implemented in two phases. First, the pretrained weights of the lightweight deep learning models are used to evaluate the forgery of the images. Secondly, the fine-tuned weights are used to compare the results of the forgery of the images with the pre-trained models. The experimental results suggest that the fusion based decision approach achieves better accuracy as compared to the state-of-the-art approaches.

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Copy-Move Forgery Detection Methods: A Critique

Kharanghar¹,

Doegar²

2020

Advances in Information Communication Technology and Computing

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Image Copy Detection Based on Convolutional Neural Networks

Cited by 6 publications

References 19 publications

The 2021 Image Similarity Dataset and Challenge

The 2021 Image Similarity Dataset and Challenge

Image forgery detection based on fusion of lightweight deep learning models

Copy-Move Forgery Detection Methods: A Critique

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