Signal Processing Cup (SP Cup)
April 07, 2025 (Monday) | 2:00 PM – 6:00 PM* (IST)
The Signal Processing Cup (SP Cup) student competition, presented by the IEEE Signal Processing Society, gives students the opportunity to work together to solve real-life problems using signal processing methods. After students submit their work, three final teams are selected to present their work and compete for the grand prize at ICASSP 2025!
While only the final three teams will be competing at ICASSP 2025, all are welcome to watch the students present their work during the final competition. Join us and come see the action!
IEEE Signal Processing Cup 2025:
Deepfake Face Detection In The Wild
Introduction
With the surge in advancements in synthetic data generation, deepfakes pose a serious threat due to their potential application in unethical and malicious purposes. This encompasses manipulating public opinion, instigating geopolitical tension, defamation, and identity threats. This year marks a significant election season globally, with more voters than ever in history expected to head to the polls across at least 64 countries. Naturally, deepfakes are of greater concern, as the generated contents in the form of image, video and audio are already widely circulated to gain political advantages. Addressing deepfakes is increasingly challenging due to the continuous development of new generation techniques, creating an ‘arms race’ between the attacker and the defender, as also mentioned in a recent BBC news report.
Of all potential deepfake content, fake images are more prevalent, as they can be easily generated by non-experts using publicly available tools that require minimal domain knowledge. In recent years, several solutions are proposed to discriminate the fake content from real. However, their effectiveness is questionable due to a lack of generalization, especially in the presence of unseen attacks not represented in the training data. To address this challenge, we present the Deepfake Face Detection In The Wild Competition (DFWild-Cup), which focuses on automatically detecting image deepfakes in real-world settings, with participants assigned to develop systems capable of distinguishing between real and computer-generated images. This DFWild-Cup competition specifically emphasizes the generalization aspects of deepfake detectors by encompassing a wide variety of data generated using diverse generation methods, attack scenarios and source datasets. While a few other machine learning challenges already addressed the deepfake detection, this represents the inaugural challenge solely focused on image deepfake detection in the wild, highlighting its emphasis on diversity, particularly in real-world scenarios.
Task Description
The DFWild-Cup is concerned with identifying images whether they are real or fake. The real image can be natural image collected with a camera or snapshots of real videos. Whereas the fake images may be generated with image various image synthesis or forgery techniques or can be snapshot of computer-generated fake videos. The participant will be provided with a set of training and test data. After training the detector with the dedicated training data, the task is to estimate a score of each of the test images. A higher score indicates a real image whereas a lower score indicates a fake image.
For full competition details, eligibility requirements, and team registration, visit the IEEE Signal Processing Society website.
IEEE SPS Signal Processing Cup Website
http://signalprocessingsociety.org/get-involved/signal-processing-cup
This competition is sponsored by MathWorks®.
Support SPS Students and watch the final three teams compete for the grand prize in the 2025 SP Cup competition!
Note: This registration is only to attend the event, not to compete. Competition details can be found on the SP Cup page.