Toward Understanding and Boosting Adversarial Transferability From a Distribution Perspective.

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood.

Three-Dimensional Mesh Steganography and Steganalysis: A Review.

Three-dimensional (3-D) meshes are commonly used to represent virtual surfaces and volumes. Over the past decade, 3-D meshes have emerged in industrial, medical, and entertainment applications, being of large practical significance for 3-D mesh steganography and steganalysis. In this article, we provide a systematic survey of the literature on 3-D mesh steganography and steganalysis.

Feature-Preserving Tensor Voting Model for Mesh Steganalysis.

The standard tensor voting technique shows its versatility in tasks such as object recognition and semantic segmentation by recognizing feature points and sharp edges that can segment a model into several patches. We propose a neighborhood-level representation-guided tensor voting model for 3D mesh steganalysis. Because existing steganalytic methods do not analyze correlations among neighborhood faces, they are not very effective at discriminating stego meshes from cover meshes.

Comments on "Steganography Using Reversible Texture Synthesis".

Message hiding in texture image synthesis is a novel steganography approach by which we resample a smaller texture image and synthesize a new texture image with a similar local appearance and an arbitrary size. However, the mirror operation over the image boundary is flawed and is easy to attack. We propose an attacking method on this steganography, which can not only detect the stego-images but can also extract the hidden messages.