Deploying multimedia deep neural networks (DNNs) on edge devices reduces retrieval and inference latency, but untrusted hardware in this setting can easily leak model parameters. Existing TEE-based protections have limitations: the partial-weight obfuscation methods are vulnerable due to statistical flaws, while full-weight obfuscation struggles to balance security and efficiency. To address these, we propose a convolution decomposition obfuscation scheme (MirageNet) for protecting edge multimedia DNNs in TEE–GPU heterogeneous environments. This scheme relieves the flaw that cosine similarity remains highly consistent between pre-trained and fine-tuned models. It obfuscates via convolution-kernel element-wise operations, decoy-kernel injection, and channel/kernel permutations, maximizing GPU utilization while minimizing TEE overhead. Experiments show that the MirageNet scheme reduces attack success to a black-box level, lowers runtime overhead by 15% compared to SOTA, and preserves inference accuracy identical to the original model—meeting practical edge multimedia retrieval demands.

The approximate nearest neighbor (ANN) search in high-dimensional space offers a wide spectrum of applications across many domains such as database, machine learning, multimedia and computer vision. A variety of ANN search algorithms have been proposed in the literature. In recent years, proximity graph-based approaches have attracted considerable attention from both industry and academic settings due to the superior search performance in terms of speed and accuracy. A recent work utilizes a graphics processing unit (GPU) to accelerate the ANN search on proximity graphs. Though significantly reducing the distance computation time by taking advantage of the massive parallelism of GPUs, the algorithm suffers from the high expenses of data structure operations. In this paper, we propose a novel GPU -accelerated algorithm that designs a novel GPU-friendly search framework on proximity graphs to fully exploit the massively parallel processing power of GPUs at key steps of the search. Also, we propose GPU-accelerated proximity graph construction algorithms which can build high-quality representative proximity graphs with efficient parallel implementations. Extensive experiments on benchmark high-dimensional datasets demonstrate the outstanding performance of our proposed algorithms in both ANN search and proximity graph construction.

Matrix Factorization (MF) has been widely applied in machine learning and data mining. Due to the large computational cost of MF, we aim to improve the efficiency of SGD-based MF computation by utilizing the massive parallel processing power of heterogeneous multiprocessors. The main challenge in parallel SGD algorithms on heterogeneous CPU-GPU systems lies in the strategy to assign tasks. We design a novel strategy to divide the matrix into a set of blocks by considering two aspects. First, we observe that the matrix should be divided nonuniformly, and relatively large blocks should be assigned to GPUs to saturate the computing power of GPUs. In addition to exploiting the characteristics of hardware, the workloads assigned to two types of hardware should be balanced. We design a cost model tailored for our problem to accurately estimate the performance of hardware on different data sizes. Extensive experiments show that our proposed algorithm achieves high efficiency with a high quality of training quality.