Italy

ICPR 2020

Scalable, Person-Agnostic Deepfake Detection In the Wild

media forensics

deepfake detection

face manipulation

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


demo

Domain adaptation has emerged as a crucial technique to address the problem of domain shift, which exists when applying an existing model to a new population of data. Adversarial learning has made impressive progress in learning a domain invariant representation via building bridges between two domains. However, existing adversarial learning methods tend to only employ a domain discriminator or generate adversarial examples that affect the original domain distribution. Moreover, little work has considered confident continuous learning using an existing source classifier for domain adaptation. In this paper, we develop adversarial continuous learning in a unified deep architecture. We also propose a novel correlated loss to minimize the discrepancy between the source and target domain. Our model increases robustness by incorporating high-confidence samples from the target domain. The transfer loss jointly considers the original source image and transfer examples in the target domain. Extensive experiments demonstrate significant improvements in classification accuracy over the state of the art.

Adversarial Continuous Learning in Unsupervised Domain Adaptation

The dominant paradigm in spatiotemporal action detection is to classify actions using spatiotemporal features learned by 2D or 3D Convolutional Networks. We argue that several actions are characterized by their context, such as relevant objects and actors present in the video. To this end, we introduce an architecture based on self-attention and Graph Convolutional Networks in order to model contextual cues, such as actor-actor and actor-object interactions, to improve human action detection in video. We are interested in achieving this in a weakly-supervised setting, i.e. using as less annotations as possible in terms of action bounding boxes. Our model aids explainability by visualizing the learned context as an attention map, even for actions and objects unseen during training. We evaluate how well our model highlights the relevant context by introducing a quantitative metric based on recall of objects retrieved by attention maps. Our model relies on a 3D convolutional RGB stream, and does not require expensive optical flow computation. We evaluate our models on the DALY dataset, which consists of human-object interaction actions. Experimental results show that our contextualized approach outperforms a baseline action detection approach by more than 2 points in Video-mAP.

Spot What Matters: Learning Context Using Graph Convolutional Networks for Weakly-Supervised Action Detection

Maria Petru Award: Maja Pantic

The Next Generation Graph Neural Networks

Measuring algorithmic bias in face analysis: towards an experimental approach

Document Integrity in an AI World

Where and What Research in PR

Discussion with industry

Opening and Awards, hosted by prof. Apostolos Antonacopoulos

ICPR 2020 Closing – Best Paper Awards, hosted by prof. Apostolos Antonacopoulos

technical paper

keynote

https://d3smihljt9218e.cloudfront.net/transcript/9547/subtitle/d8717160dcdcee0b30b29bceb807f432.vtt

English (automatic)

Premium content

Scalable, Person-Agnostic Deepfake Detection In the Wild

Downloads

Next from ICPR 2020

Adversarial Continuous Learning in Unsupervised Domain Adaptation

Similar lecture

Unsupervised Face Manipulation Via Hallucination

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES