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MSF-P: Multimedia security and forensics
11:15am - 11:20am
Defining Embedding Distortion for Sample Adaptive Offset-Based HEVC Video Steganography
University of Science and Technology of China, Hefei 230027, China
As a newly added in-loop filtering technique in High Efficiency Video Coding (HEVC), sample adaptive offset (SAO) can be utilized to embed messages for video steganography. This paper presents a novel SAO-based HEVC video steganographic scheme. The main principle is to design a suitable distortion function which expresses the embedding impacts on offsets based on minimizing embedding distortion. Two factors including the sample rate-distortion cost fluctuation and the sample statistical characteristic are considered in embedding distortion definition. Adaptive message embedding is implemented using syndrometrellis codes (STC). Experimental results demonstrate the merits of the proposed scheme in terms of undetectability and video coding performance.
11:20am - 11:25am
Towards Criminal Sketching with Generative Adversarial Network
Shanghai University, China, People's Republic of
Criminal sketching aims to draw an approximation portrait of the criminal suspect based on details of the criminal suspect that the observer can remember. However, even for a professional artist, it needs much time to complete sketching and draw a good portrait. In this work, we focus on forensic sketching using a generative adversarial network (GAN) based architecture, which allows us to synthesize a real-like portrait of a criminal suspect described by an eyewitness. The proposed framework consists of two steps: sketch generation and portrait generation. For the former, a facial outline is sketched based on the descriptive details. For the latter, the facial details are completed to generate a portrait. To make the portrait more realistic, we use a portrait discriminator, which can not only learn the discriminative features between the faces synthesized by the generator and the real faces, but also recognize the face attributes. Experiments have shown that the proposed work achieves promising performance for criminal sketching.
11:25am - 11:30am
Natural Scene Statistics for Detecting Adversarial Examples in Deep Neural Networks
1Univ. Rennes, INSA Rennes, CNRS, IETR - UMR 6164, Rennes, France; 2National Institute of Telecommunications and ICT, Oran, Algeria
The deep neural networks (DNNs) have been adopted in a wide spectrum of applications. However, it has been demonstrated that their are vulnerable to adversarial examples (AEs): carefully-crafted perturbations added to a clean input image. These AEs fool the DNNs which classify them incorrectly. Therefore, it is imperative to develop a detection method of AEs allowing to reject them. In this paper, we propose to characterize the adversarial perturbations through the use of natural scene statistics. We demonstrate that these statistical properties are altered by the presence of adversarial perturbations. Based on this finding, we design a classifier that exploits these scene statistics to determine if an input is adversarial or not. The proposed method has been evaluated against four prominent adversarial attacks and on three standards datasets. The experimental results have shown that the proposed detection method achieves a high detection accuracy, even against strong attacks, while providing a low false positive rate.