BAFLE-DCT: Bypassing Adversarial Filters via Frequency-Selective Embedding in the DCT Domain

Deep learning-based vision systems are increasingly deployed in high-stakes applications, yet remain vulnerable to imperceptible manipulations that exploit model blind spots. We present BAFLE-DCT, a frequency-domain steganography framework that achieves high-capacity, imperceptible data embedding while evading state-of-the-art deep steganalysis. Unlike traditional spatial-domain methods that alter pixel values and trigger visual or statistical artifacts, BAFLE-DCT operates in the DCT domain, selectively modifying mid-frequency coefficients in perceptually insignificant regions identified via saliency analysis. A lightweight feedforward network further refines block selection using entropy and DCT variance features to balance embedding capacity and visual fidelity. Stego images generated by BAFLE-DCT consistently bypass advanced detectors such as YeNet and SRNet, yielding near-random detection rates (~50%) across payload sizes. Importantly, embedded images maintain classification consistency under CLIP, demonstrating semantic preservation. We also release a large-scale, full-color steganographic dataset for frequency-domain research, addressing limitations of grayscale, spatial-domain benchmarks. Our results expose critical vulnerabilities in visual content authentication pipelines and motivate the development of frequency-aware detection strategies. Code and data will be released publicly upon acceptance.