Sunspot shearing and sudden retraction motion associated with the 2013 August 17 M3.3 Flare

In this Letter, we give a detailed analysis to the M3.3 class flare that occurred on August 17, 2013 (SOL2013-08-17T18:16). It presents a clear picture of mutual magnetic interaction initially from the photosphere to the corona via the abrupt rapid shearing motion of a small sunspot before the flare, and then suddenly from the corona back to the photosphere via the sudden retraction motion of the same sunspot during the flare impulsive phase. About 10 hours before the flare, a small sunspot in the active region NOAA 11818 started to move northeast along a magnetic polarity inversion line (PIL), creating a shearing motion that changed the quasi-static state of the active region. A filament right above the PIL was activated following the movement of the sunspot and then got partially erupted. The eruption eventually led to the M3.3 flare. The sunspot was then suddenly pulled back to the opposite direction upon the flare onset. During the backward motion, the Lorentz force underwent a simultaneous impulsive change both in magnitude and direction. Its directional change is found to be conformable with the retraction motion. The observation provides direct evidence for the role of the shearing motion of the sunspot in powering and triggering the flare. It especially confirms that the abrupt motion of a sunspot during a solar flare is the result of a back reaction caused by the reconfiguration of the coronal magnetic field

RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection

The widespread use of face retouching filters on short-video platforms has raised concerns about the authenticity of digital appearances and the impact of deceptive advertising. To address these issues, there is a pressing need to develop advanced face retouching techniques. However, the lack of large-scale and fine-grained face retouching datasets has been a major obstacle to progress in this field. In this paper, we introduce RetouchingFFHQ, a large-scale and fine-grained face retouching dataset that contains over half a million conditionally-retouched images. RetouchingFFHQ stands out from previous datasets due to its large scale, high quality, fine-grainedness, and customization. By including four typical types of face retouching operations and different retouching levels, we extend the binary face retouching detection into a fine-grained, multi-retouching type, and multi-retouching level estimation problem. Additionally, we propose a Multi-granularity Attention Module (MAM) as a plugin for CNN backbones for enhanced cross-scale representation learning. Extensive experiments using different baselines as well as our proposed method on RetouchingFFHQ show decent performance on face retouching detection. With the proposed new dataset, we believe there is great potential for future work to tackle the challenging problem of real-world fine-grained face retouching detection.Comment: Under revie