41 research outputs found
Learning Rich Features for Image Manipulation Detection
Image manipulation detection is different from traditional semantic object
detection because it pays more attention to tampering artifacts than to image
content, which suggests that richer features need to be learned. We propose a
two-stream Faster R-CNN network and train it endto- end to detect the tampered
regions given a manipulated image. One of the two streams is an RGB stream
whose purpose is to extract features from the RGB image input to find tampering
artifacts like strong contrast difference, unnatural tampered boundaries, and
so on. The other is a noise stream that leverages the noise features extracted
from a steganalysis rich model filter layer to discover the noise inconsistency
between authentic and tampered regions. We then fuse features from the two
streams through a bilinear pooling layer to further incorporate spatial
co-occurrence of these two modalities. Experiments on four standard image
manipulation datasets demonstrate that our two-stream framework outperforms
each individual stream, and also achieves state-of-the-art performance compared
to alternative methods with robustness to resizing and compression.Comment: CVPR 2018 Camera Read
AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI
Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images
D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization
Recently, many detection methods based on convolutional neural networks
(CNNs) have been proposed for image splicing forgery detection. Most of these
detection methods focus on the local patches or local objects. In fact, image
splicing forgery detection is a global binary classification task that
distinguishes the tampered and non-tampered regions by image fingerprints.
However, some specific image contents are hardly retained by CNN-based
detection networks, but if included, would improve the detection accuracy of
the networks. To resolve these issues, we propose a novel network called
dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs
an unfixed encoder and a fixed encoder. The unfixed encoder autonomously learns
the image fingerprints that differentiate between the tampered and non-tampered
regions, whereas the fixed encoder intentionally provides the direction
information that assists the learning and detection of the network. This
dual-encoder is followed by a spatial pyramid global-feature extraction module
that expands the global insight of D-Unet for classifying the tampered and
non-tampered regions more accurately. In an experimental comparison study of
D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in
image-level and pixel-level detection, without requiring pre-training or
training on a large number of forgery images. Moreover, it was stably robust to
different attacks.Comment: 13 pages, 13 figure