Xiaoqing Qiu | Searcharxiv

arXiv2022

Is the core-cusp problem a matter of perspective: Jeans Anisotropic Modeling against numerical simulations

Mock member stars for 28 dwarf galaxies are constructed from the cosmological Auriga simulation, which reflect the dynamical status of realistic stellar tracers. The axis-symmetric Jeans Anisotropic Multi-Gaussian Expansion (JAM) modeling is applied to 6,000 star particles for each system, to recover the underlying matter distribution. The stellar or dark matter component individually is poorly recovered, but the total profile is constrained more reasonably. The mass within the half-mass radius of tracers is recovered the tightest, and the mass between 200 and 300 pc, $M(200-300\mathrm{pc})$, is constrained ensemble unbiasedly, with a scatter of 0.167 dex. If using 2,000 particles and only line-of-sight velocities with typical errors, the scatter in $M(200-300\mathrm{pc})$ is increased by $\sim$50%. Quiescent Sagittarius dSph-like systems and star-forming systems with strong outflows show distinct features, with $M(200-300\mathrm{pc})$ mostly under-estimated for the former, and likely over-estimated for the latter. The biases correlate with the dynamical status, which is a result of contraction motions due to tidal effects in quiescent systems or galactic winds in star-forming systems, driving them out of equilibrium. After including Gaia DR3 proper motion errors, we find proper motions can be as useful as line-of-sight velocities for nearby systems at $<\sim$60 kpc. By extrapolating the actual density profiles and the dynamical constraints down to scales below the resolution, we find the mass within 150 pc can be constrained ensemble unbiasedly, with a scatter of $\sim$0.255 dex. In the end, we show that the contraction of member stars in nearby systems is detectable based on Gaia DR3 proper motion errors.

astro-ph.GA↗

arXiv2015

Identification of Image Operations Based on Steganalytic Features

Image forensics have attracted wide attention during the past decade. Though many forensic methods have been proposed to identify image forgeries, most of them are targeted ones, since their proposed features are highly dependent on the image operation under investigation. The performance of the well-designed features for detecting the targeted operation usually degrades significantly for other operations. On the other hand, a wise attacker can perform anti-forensics to fool the existing forensic methods, making countering anti-forensics become an urgent need. In this paper, we try to find a universal feature to detect various image processing and anti-forensic operations. Based on our extensive experiments and analysis, we find that any image processing/anti-forensic operations would inevitably modify many image pixels. This would change some inherent statistics within original images, which is similar to the case of steganography. Therefore, we model image processing/anti-forensic operations as steganography problems, and propose a detection strategy by applying steganalytic features. With some advanced steganalytic features, we are able to detect various image operations and further identify their types. In our experiments, we have tested several steganalytic features on 11 different kinds of typical image processing operations and 4 kinds of anti-forensic operations. The experimental results have shown that the proposed strategy significantly outperforms the existing forensic methods in both effectiveness and universality.

cs.MM↗