On the "Security analysis and improvements of arbitrated quantum signature schemes"

Recently, Zou et al. [Phys. Rev. A 82, 042325 (2010)] pointed out that two arbitrated quantum signature (AQS) schemes are not secure, because an arbitrator cannot arbitrate the dispute between two users when a receiver repudiates the integrity of a signature. By using a public board, they try to propose two AQS schemes to solve the problem. This work shows that the same security problem may exist in their schemes and also a malicious party can reveal the other party's secret key without being detected by using the Trojan-horse attacks. Accordingly, two basic properties of a quantum signature, i.e. unforgeability and undeniability, may not be satisfied in their scheme

Mitigating Biased Activation in Weakly-supervised Object Localization via Counterfactual Learning

In this paper, we focus on an under-explored issue of biased activation in prior weakly-supervised object localization methods based on Class Activation Mapping (CAM). We analyze the cause of this problem from a causal view and attribute it to the co-occurring background confounders. Following this insight, we propose a novel Counterfactual Co-occurring Learning (CCL) paradigm to synthesize the counterfactual representations via coupling constant foreground and unrealized backgrounds in order to cut off their co-occurring relationship. Specifically, we design a new network structure called Counterfactual-CAM, which embeds the counterfactual representation perturbation mechanism into the vanilla CAM-based model. This mechanism is responsible for decoupling foreground as well as background and synthesizing the counterfactual representations. By training the detection model with these synthesized representations, we compel the model to focus on the constant foreground content while minimizing the influence of distracting co-occurring background. To our best knowledge, it is the first attempt in this direction. Extensive experiments on several benchmarks demonstrate that Counterfactual-CAM successfully mitigates the biased activation problem, achieving improved object localization accuracy.Comment: 13 pages, 5 figures, 4 table