A Counter-Forensic Method for CNN-Based Camera Model Identification

Abstract

An increasing number of digital images are being shared and accessed through websites, media, and social applications. Many of these images have been modified and are not authentic. Recent advances in the use of deep convolutional neural networks (CNNs) have facilitated the task of analyzing the veracity and authenticity of largely distributed image datasets. We examine in this paper the problem of identifying the camera model or type that was used to take an image and that can be spoofed. Due to the linear nature of CNNs and the high-dimensionality of images, neural networks are vulnerable to attacks with adversarial examples. These examples are imperceptibly different from correctly classified images but are misclassified with high confidence by CNNs. In this paper, we describe a counter-forensic method capable of subtly altering images to change their estimated camera model when they are analyzed by any CNN-based camera model detector. Our method can use both the Fast Gradient Sign Method (FGSM) or the Jacobian-based Saliency Map Attack (JSMA) to craft these adversarial images and does not require direct access to the CNN. Our results show that even advanced deep learning architectures trained to analyze images and obtain camera model information are still vulnerable to our proposed method.

arXiv commentPresented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshop on Media Forensics

References in corpus (5)

• Explaining and Harnessing Adversarial Examples
• Intriguing properties of neural networks
• Densely Connected Convolutional Networks
• Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
• First Steps Toward Camera Model Identification with Convolutional Neural Networks

Cited by in corpus (9)

• Media Forensics and DeepFakes: an overview
• Adversarial Examples in Modern Machine Learning: A Review
• Physics-Guided Spoof Trace Disentanglement for Generic Face Anti-Spoofing
• A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network
• Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing
• Conditional Adversarial Camera Model Anonymization
• The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs
• Adversarial Attack on Deep Learning-Based Splice Localization
• Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples