A deep learning approach to patch-based image inpainting forensics

Zhu, Xiaoyan; Qian, Yongjun; Zhao, Xianfeng; Sun, Biao; Sun, Ya

doi:10.1016/j.image.2018.05.015

Cited by 104 publications

(90 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some examples of image inpainting detection can be seen in Figures 8 and 9, we find these three test algorithms from left to right: [35,36] and our method can basically distinguish the inpainted area which has been listed in Figure 6. e last column of this figure shows that we can get more accurate pixel-level positioning in the detection using our method in different inpainted shapes.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 66%

“…We choose True Positives Rate (TPR), False Positives Rate (FPR), and Accuracy Precision (AP) as evaluation metrics standard. Compare the performance of their detection in the same dataset with the approaches in [35,36]. One of the algorithms for selecting contrast is that the traditional method has a better effect, and the other is the latest method of using the deep learning algorithm.…”

Section: Evaluation Metricsmentioning

confidence: 99%

See 1 more Smart Citation

An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting

Wang

Niu

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Image inpainting algorithms have a wide range of applications, which can be used for object removal in digital images. With the development of semantic level image inpainting technology, this brings great challenges to blind image forensics. In this case, many conventional methods have been proposed which have disadvantages such as high time complexity and low robustness to postprocessing operations. Therefore, this paper proposes a mask regional convolutional neural network (Mask R-CNN) approach for patch-based inpainting detection. According to the current research, many deep learning methods have shown the capacity for segmentation tasks when labeled datasets are available, so we apply a deep neural network to the domain of inpainting forensics. This deep learning model can distinguish and obtain different features between the inpainted and noninpainted regions. To reduce the missed detection areas and improve detection accuracy, we also adjust the sizes of the anchor scales due to the inpainting images and replace the original nonmaximum suppression single threshold with an improved nonmaximum suppression (NMS). The experimental results demonstrate this intelligent method has better detection performance over recent approaches of image inpainting forensics.

show abstract

Section: Mathematical Problems In Engineeringmentioning

confidence: 66%

Section: Evaluation Metricsmentioning

confidence: 99%

An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting

Wang

Niu

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Approaches based on deep neural networks have also been used to investigate low-level image features. Methods that focus on low-level features mostly focus on detecting local inconsistencies or statistical features relating to the manipulated images [12][13][14][15][16].…”

Section: Image Forensicsmentioning

confidence: 99%

Exposing Digital Image Forgeries by Detecting Contextual Abnormality Using Convolutional Neural Networks

Jang

Hou

2020

Sensors

View full text Add to dashboard Cite

Traditionally, digital image forensics mainly focused on the low-level features of an image, such as edges and texture, because these features include traces of the image’s modification history. However, previous methods that employed low-level features are highly vulnerable, even to frequently used image processing techniques such as JPEG and resizing, because these techniques add noise to the low-level features. In this paper, we propose a framework that uses deep neural networks to detect image manipulation based on contextual abnormality. The proposed method first detects the class and location of objects using a well-known object detector such as a region-based convolutional neural network (R-CNN) and evaluates the contextual scores according to the combination of objects, the spatial context of objects and the position of objects. Thus, the proposed forensics can detect image forgery based on contextual abnormality as long as the object can be identified even if noise is applied to the image, contrary to methods that employ low-level features, which are vulnerable to noise. Our experiments showed that our method is able to effectively detect contextual abnormality in an image.

show abstract

“…In the last few years, deep learning based approaches have also been applied in the field of image forensics. Several CNN-based systems have been proposed to detect traces of image inpainting (Zhu et al, 2018), effects of image resizing and compression (Bayar and Stamm, 2017), and median filtering detection , among other image forensic tasks.…”

Section: Approaches Based On Deep Learningmentioning

confidence: 99%

Device-based Image Matching with Similarity Learning by Convolutional Neural Networks that Exploit the Underlying Camera Sensor Pattern Noise

Bennabhaktula

Alegre

Karastoyanova

et al. 2020

Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods

View full text Add to dashboard Cite

One of the challenging problems in digital image forensics is the capability to identify images that are captured by the same camera device. This knowledge can help forensic experts in gathering intelligence about suspects by analyzing digital images. In this paper, we propose a two-part network to quantify the likelihood that a given pair of images have the same source camera, and we evaluated it on the benchmark Dresden data set containing 1851 images from 31 different cameras. To the best of our knowledge, we are the first ones addressing the challenge of device-based image matching. Though the proposed approach is not yet forensics ready, our experiments show that this direction is worth pursuing, achieving at this moment 85 percent accuracy. This ongoing work is part of the EU-funded project 4NSEEK concerned with forensics against child sexual abuse.

show abstract

A deep learning approach to patch-based image inpainting forensics

Cited by 104 publications

References 31 publications

An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting

An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting

Exposing Digital Image Forgeries by Detecting Contextual Abnormality Using Convolutional Neural Networks

Device-based Image Matching with Similarity Learning by Convolutional Neural Networks that Exploit the Underlying Camera Sensor Pattern Noise

Contact Info

Product

Resources

About