Learning Rich Features for Image Manipulation Detection

Abstract

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.

CVPR 2018 Camera Ready

References in corpus (5)

• Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)
• Two-Stream Neural Networks for Tampered Face Detection
• Dynamic Zoom-in Network for Fast Object Detection in Large Images
• The Role of Context Selection in Object Detection
• Deep Regionlets for Object Detection

Cited by in corpus (11)

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• A Convolutional LSTM based Residual Network for Deepfake Video Detection
• Detection, Attribution and Localization of GAN Generated Images
• Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices
• Efficient Image Splicing Localization via Contrastive Feature Extraction
• Arbitrary-sized Image Training and Residual Kernel Learning: Towards Image Fraud Identification
• Goal-oriented Object Importance Estimation in On-road Driving Videos
• Camera Invariant Feature Learning for Generalized Face Anti-spoofing