Short-video platforms now host vast multimodal ads whose deceptive visuals, speech and subtitles demand finer-grained, policy-driven moderation than community safety filters. We present BLM-Guard, a content-audit framework for commercial ads that fuses Chain-of-Thought reasoning with rule-based policy principles and a critic-guided reward. A rule-driven ICoT data-synthesis pipeline jump-starts training by generating structured scene descriptions, reasoning chains and labels, cutting annotation costs. Reinforcement learning then refines the model using a composite reward balancing causal coherence with policy adherence. A multitask architecture models intra-modal manipulations (e.g., exaggerated imagery) and cross-modal mismatches (e.g., subtitle–speech drift), boosting robustness. Experiments on real short-video ads show BLM-Guard surpasses strong baselines in accuracy, consistency and generalization.

The Berlekamp-Massey algorithm is a primary algorithm for decoding Reed-Solomon codes. As an inherent property of the algorithm, the early termination can effectively reduce the latency and power of decoding. It has been known that the algorithm can be terminated at the completion of the$(t+e)$-th iteration (where$e$is the number of errors and$t$is the error-correction capability of the code). In this paper, we explore a new mechanism for the early termination. Specifically, assuming$e\leq t$, we present a detection method that can identify the$2e$-th iteration. Since the error locator polynomial will have been found at the completion of the$2e$-th iteration, we can terminate the algorithm at this point based on the proposed method. As an application, a hardware-friendly algorithm variant, dubbed Reformulated Early-Terminating Parallel Inversionless Berlekamp-Massey (RETPIBM) algorithm, is presented, which yields a systolic architecture. The derivative architecture consists of$3t+1$processing elements (PEs) and has the critical path of one multiplier and one adder. To the best of the authors' knowledge, this is literally the first architecture that achieves the early termination for Berlekamp-Massey algorithm.

Extensive research revealed that deep neural networks are vulnerable to adversarial examples. In addition, recent studies have demonstrated that convolutional neural networks tend to recognize the texture (high-frequency components) rather than the shape (low-frequency components) of images. Thus, crafting adversarial perturbation in the frequency domain is proposed to enhance the attack strength. However, these methods either will increase the perceptibility of adversarial examples to the human visual system (HVS) or increase the computational effort in generating adversarial examples. To generate adversarial examples with better imperceptibility while consuming less computational effort, we propose an adversarial attack method to construct adversarial examples in the frequency domain with guidance from the local texture complexity of the image. Experiments on ImageNet and CIFAR-10 show that the proposed method is effective in generating adversarial examples imperceptible to the HVS.

Deep neural network models are vulnerable to subtle but adversarial perturbations that alter the model. Adversarial perturbations are typically computed for RGB images and, therefore, are evenly distributed among RGB channels. Compared with RGB images, HSV images can express the Hue, saturation, and brightness more intuitively. We find that the adversarial perturbation in the S-channel ensures a high attack success rate, while the perturbation is small, and the visual quality of the adversarial examples is good. Using this finding, we propose an attack method, SPGD, to improve the visual quality of adversarial examples by generating perturbations on the S-channel. Based on the attack principle of the PGD method, the RGB image was converted into an HSV image. The gradient calculated by the model on the S channel was superimposed on the S channel and then combined with the non-interference H and V channels to convert back to the RGB image. The iteration stops until the attack succeed. We compare the SPGD method with the existing state-of-the-art attack methods. The results show that SPGD minimizes pixel perturbation while maintaining a high attack success rate and achieves the best results in terms of structural similarity, imperceptibility, the minimum number of iterations, and the shortest run time.