Preserving Integrity in Online Social Networks

Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinformation and hate speech, selling illegal drugs, and coordinating sex trafficking or child exploitation. This paper surveys the state of the art in keeping online platforms and their users safe from such harm, also known as the problem of preserving integrity. This survey comes from the perspective of having to combat a broad spectrum of integrity violations at Facebook. We highlight the techniques that have been proven useful in practice and that deserve additional attention from the academic community. Instead of discussing the many individual violation types, we identify key aspects of the social-media eco-system, each of which is common to a wide variety violation types. Furthermore, each of these components represents an area for research and development, and the innovations that are found can be applied widely.

The DeepFake Detection Challenge Dataset

Deepfakes are a recent off-the-shelf manipulation technique that allows anyone to swap two identities in a single video. In addition to Deepfakes, a variety of GAN-based face swapping methods have also been published with accompanying code. To counter this emerging threat, we have constructed an extremely large face swap video dataset to enable the training of detection models, and organized the accompanying DeepFake Detection Challenge (DFDC) Kaggle competition. Importantly, all recorded subjects agreed to participate in and have their likenesses modified during the construction of the face-swapped dataset. The DFDC dataset is by far the largest currently and publicly available face swap video dataset, with over 100,000 total clips sourced from 3,426 paid actors, produced with several Deepfake, GAN-based, and non-learned methods. In addition to describing the methods used to construct the dataset, we provide a detailed analysis of the top submissions from the Kaggle contest. We show although Deepfake detection is extremely difficult and still an unsolved problem, a Deepfake detection model trained only on the DFDC can generalize to real "in-the-wild" Deepfake videos, and such a model can be a valuable analysis tool when analyzing potentially Deepfaked videos. Training, validation and testing corpuses can be downloaded from https://ai.facebook.com/datasets/dfdc.

Deep Poisoning Functions: Towards Robust Privacy-safe Image Data Sharing

As deep networks are applied to an ever-expanding set of computer vision tasks, protecting general privacy in image data has become a critically important goal. This paper presents a new framework for privacy-preserving data sharing that is robust to adversarial attacks and overcomes the known issues existing in previous approaches. We introduce the concept of a Deep Poisoning Function (DPF), which is a module inserted into a pre-trained deep network designed to perform a specific vision task. The DPF is optimized to deliberately poison image data to prevent known adversarial attacks, while ensuring that the altered image data is functionally equivalent to the non-poisoned data for the original task. Given this equivalence, both poisoned and non-poisoned data can be used for further retraining or fine-tuning. Experimental results on image classification and face recognition tasks prove the efficacy of the proposed method.

The Deepfake Detection Challenge (DFDC) Preview Dataset

In this paper, we introduce a preview of the Deepfakes Detection Challenge (DFDC) dataset consisting of 5K videos featuring two facial modification algorithms. A data collection campaign has been carried out where participating actors have entered into an agreement to the use and manipulation of their likenesses in our creation of the dataset. Diversity in several axes (gender, skin-tone, age, etc.) has been considered and actors recorded videos with arbitrary backgrounds thus bringing visual variability. Finally, a set of specific metrics to evaluate the performance have been defined and two existing models for detecting deepfakes have been tested to provide a reference performance baseline. The DFDC dataset preview can be downloaded at: deepfakedetectionchallenge.ai

Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation

This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge. Memes are pixel-based multimedia documents that contain photos or illustrations together with phrases which, when combined, usually adopt a funny meaning. However, hate memes are also used to spread hate through social networks, so their automatic detection would help reduce their harmful societal impact. Our results indicate that the model can learn to detect some of the memes, but that the task is far from being solved with this simple architecture. While previous work focuses on linguistic hate speech, our experiments indicate how the visual modality can be much more informative for hate speech detection than the linguistic one in memes. In our experiments, we built a dataset of 5,020 memes to train and evaluate a multi-layer perceptron over the visual and language representations, whether independently or fused. The source code and mode and models are available https://github.com/imatge-upc/hate-speech-detection .

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples. The first stage of the network consists of a generator model whose weights are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency maps. The resulting prediction is processed by a discriminator network trained to solve a binary classification task between the saliency maps generated by the generative stage and the ground truth ones. Our experiments show how adversarial training allows reaching state-of-the-art performance across different metrics when combined with a widely-used loss function like BCE. Our results can be reproduced with the source code and trained models available at https://imatge-upc.github.io/saliency-salgan-2017/.

Towards social pattern characterization in egocentric photo-streams

Following the increasingly popular trend of social interaction analysis in egocentric vision, this manuscript presents a comprehensive study for automatic social pattern characterization of a wearable photo-camera user, by relying on the visual analysis of egocentric photo-streams. The proposed framework consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task, and LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns, which is essentially to infer the diversity and frequency of the social relations of the user through discovery of recurrences of the same people across the whole set of social events of the user. Experimental evaluation over a dataset acquired by 9 users demonstrates promising results on the task of social pattern characterization from egocentric photo-streams.