Attacking Recommender Systems with Augmented User Profiles

Recommendation Systems (RS) have become an essential part of many online services. Due to its pivotal role in guiding customers towards purchasing, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this paper, we study the shilling attack: a subsistent and profitable attack where an adversarial party injects a number of user profiles to promote or demote a target item. Conventional shilling attack models are based on simple heuristics that can be easily detected, or directly adopt adversarial attack methods without a special design for RS. Moreover, the study on the attack impact on deep learning based RS is missing in the literature, making the effects of shilling attack against real RS doubtful. We present a novel Augmented Shilling Attack framework (AUSH) and implement it with the idea of Generative Adversarial Network. AUSH is capable of tailoring attacks against RS according to budget and complex attack goals, such as targeting a specific user group. We experimentally show that the attack impact of AUSH is noticeable on a wide range of RS including both classic and modern deep learning based RS, while it is virtually undetectable by the state-of-the-art attack detection model.Comment: CIKM 2020. 10 pages, 2 figure

Drum Synthesis and Rhythmic Transformation with Adversarial Autoencoders

Creative rhythmic transformations of musical audio refer to automated methods for manipulation of temporally-relevant sounds in time. This paper presents a method for joint synthesis and rhythm transformation of drum sounds through the use of adversarial autoencoders (AAE). Users may navigate both the timbre and rhythm of drum patterns in audio recordings through expressive control over a low-dimensional latent space. The model is based on an AAE with Gaussian mixture latent distributions that introduce rhythmic pattern conditioning to represent a wide variety of drum performances. The AAE is trained on a dataset of bar-length segments of percussion recordings, along with their clustered rhythmic pattern labels. The decoder is conditioned during adversarial training for mixing of data-driven rhythmic and timbral properties. The system is trained with over 500000 bars from 5418 tracks in popular datasets covering various musical genres. In an evaluation using real percussion recordings, the reconstruction accuracy and latent space interpolation between drum performances are investigated for audio generation conditioned by target rhythmic patterns

PaintsTorch: a User-Guided Anime Line Art Colorization Tool with Double Generator Conditional Adversarial Network

International audienceThe lack of information provided by line arts makes user guidedcolorization a challenging task for computer vision. Recent contributions from the deep learning community based on Generative Adversarial Network (GAN) have shown incredible results compared to previous techniques. These methods employ user input color hints as a way to condition the network. The current state of the art has shown the ability to generalize and generate realistic and precise colorization by introducing a custom dataset and a new model with its training pipeline. Nevertheless, their approach relies on randomly sampled pixels as color hints for training. Thus, in this contribution, we introduce a stroke simulation based approach for hint generation, making the model more robust to messy inputs. We also propose a new cleaner dataset, and explore the use of a double generator GAN to improve visual fidelity