FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis

The ubiquity of social media platforms facilitates malicious linguistic steganography, posing significant security risks. However, detection is severely hampered by two fundamental issues during model training. Firstly, extreme class imbalance (less than 1% steganographic samples) induces a strong decision bias. Secondly, the invisibility of generative steganography means its features are nearly indistinguishable from benign text; this similarity, compounded by their extreme rarity, leads to severe feature marginalization, where faint steganographic signals are completely overwhelmed. To directly address these optimization-level challenges, we propose FADRW (Feature-Aware Modulated and Dynamically Reweighted Loss), a novel loss function framework engineered for few-shot steganalysis. FADRW employs Dynamic Reweighting to progressively counteract decision bias, and a Feature-Aware Modulation module to structurally reshape the feature space, preventing feature marginalization by enhancing the separability of these subtle features. Extensive experiments on datasets from three real-world social platforms demonstrate that FADRW significantly outperforms state-of-the-art methods, particularly in the challenging few-shot steganographic sample scenario.

An online supervised learning algorithm based on triple spikes for spiking neural networks

Using precise times of every spike, spiking supervised learning has more effects on complex spatial-temporal pattern than that only through neuronal firing rates. The purpose of spiking supervised learning after temporal encoding is to make neural networks emit desired spike trains with precise firing time. Existing algorithms of spiking supervised learning have excellent performances, but mechanisms of them are most in an offline pattern or still have some problems. Based on an online regulative mechanism of biological neuronal synapses, this paper proposes an online supervised learning algorithm of multiple spike trains for spiking neural networks. The proposed algorithm can make a regulation of weights as soon as firing time of an output spike is obtained. Relationship among desired output, actual output and input spike trains is firstly analyzed and synthesized to select spikes simply and correctly for a direct regulation, and then a computational method is constructed based on simple triple spikes using this direct regulation. Results of experiments show that this online supervised algorithm improves learning performance obviously compared with offline pattern and has higher learning accuracy and efficiency than other learning algorithms.