Content-aware detection of JPEG grid inconsistencies for intuitive image forensics

Iakovidou, Chryssanthi; Zampoglou, Markos; Papadopoulos, Symeon; Kompatsiaris, Ioannis

doi:10.1016/j.jvcir.2018.05.011

Cited by 53 publications

(36 citation statements)

References 33 publications

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“…Additionally, the performance of rotated images showed low accuracy compared to the other images because rotation seems to generate artifacts in the images. Geometric operations such as rotation and scaling can destroy pixels and generate artifacts in the rotated images 15,16 .…”

Section: Resultsmentioning

confidence: 99%

CNN-based diagnosis models for canine ulcerative keratitis

Kim

Lee

Choi

et al. 2019

Sci Rep

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the purpose of this methodological study was to develop a convolutional neural network (cnn), which is a recently developed deep-learning-based image recognition method, to determine corneal ulcer severity in dogs. the cnn model was trained with images for which corneal ulcer severity (normal, superficial, and deep) were previously classified by veterinary ophthalmologists' diagnostic evaluations of corneal photographs from patients who visited the Veterinary Medical teaching Hospital (VMtH) at Konkuk University and 3 different veterinary ophthalmology specialty hospitals in Korea. The original images (depicting normal corneas (36) and corneas with superficial (47) ulcers, deep (47) ulcers), flipped images (total 520), rotated images (total 520), and both flipped and rotated images (total 1,040) were labeled, learned and evaluated with GoogLenet, Resnet, and VGGnet models, and the severity of each corneal ulcer image was determined. to accomplish this task, models based on tensorflow, an opensource software library developed by Google, were used, and the labeled images were converted into TensorFlow record (TFRecord) format. The models were fine-tuned using a CNN model trained on the imagenet dataset and then used to predict severity. Most of the models achieved accuracies of over 90% when classifying superficial and deep corneal ulcers, and ResNet and VGGNet achieved accuracies over 90% for classifying normal corneas, corneas with superficial ulcers, and corneas with deep ulcers. This study proposes a method to effectively determine corneal ulcer severity in dogs by using a CNN and concludes that multiple image classification models can be used in the veterinary field. Deep learning diagnostic tools for image recognition have recently been tested in many medical fields. In the field of imaging diagnostics, the use of such tools have been reported in The Veterinary Journal 1,2. Recent advances in computer hardware technology, such as high performance graphic processing units (GPUs), have permitted the development of deep neural networks (DNNs). Deep learning algorithms are an evolution of neural networks and are currently used in a variety of medical and industrial applications 3,4. Deep learning fundamentally consists of a deep neural network structure with several layers. An artificial neural network based on the backpropagation 5 algorithm was highly anticipated in the 1990s; such a network would utilize logic that corrects the error of each neuron after analyzing the error in the reverse direction at the output side when the error occurs. However, research has stagnated because learning in artificial neural network models becomes more difficult as the number of layers increases. Since the mid-2000s, artificial neural networks have been improved with respect to learning methods: huge amounts of data have been made available, and the hardware environment has been improved, leading to remarkable performance improvements. In addition, the dataset overfitting problem, which was a persistent issue with artificial n...

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Section: Resultsmentioning

confidence: 99%

CNN-based diagnosis models for canine ulcerative keratitis

Kim

Lee

Choi

et al. 2019

Sci Rep

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“…• BLK [18] and CAGI [19] that base their detection on analysis of the JPEG compression in the spatial domain;…”

Section: Methodsmentioning

confidence: 99%

Operation-wise Attention Network for Tampering Localization Fusion

Charitidis¹,

Kordopatis-Zilos²,

Papadopoulos³

et al. 2021

Preprint

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In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of five individual tampering localization methods for splicing localization on JPEG images. The proposed deep learning fusion model is an adapted architecture, initially proposed for the image restoration task, that performs multiple operations in parallel, weighted by an attention mechanism to enable the selection of proper operations depending on the input signals. This weighting process can be very beneficial for cases where the input signal is very diverse, as in our case where the output signals of multiple image forensics algorithms are combined. Evaluation in three publicly available forensics datasets demonstrates that the performance of the proposed approach is competitive, outperforming the individual forensics techniques as well as another recently proposed fusion framework in the majority of cases.

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“…when part of the image is erased and then automatically filled using an in-painting algorithm is in principle similar, since the computer-generated part will carry a different profile than the rest of the image. Algorithms designed to detect such forgeries may exploit inconsistencies in the local JPEG compression history [18,25], in local noise patterns [29,11], or in the traces left by the capturing devices' Color Filter Array (CFA) [15,19]. It is interesting to note that, in many cases, such algorithms are also able to detect copy-move forgeries, as they also often cause detectable local disruptions.…”

Section: Image Forensicsmentioning

confidence: 99%