AI models for Live steganography - hide small text/data in video frames
This service embeds a small, invisible payload into a live video stream as it passes — a stream identifier, a recipient code, a timestamp — so that any copy or screenshot taken later can be traced back to the stream and the moment it came from.
The application is leak tracing. Where a stream goes to several recipients and each copy carries a different invisible code, a recording that appears elsewhere identifies which recipient it came from. Doing this live imposes tight limits: the mark must be embedded within the frame interval, so payloads are small — tens of bits — and the model must be light. The payload also has to survive re-encoding and screen capture, which is what the model-based methods provide and simple ones do not. As always, this identifies content; it does not prevent copying.
Models in this group take a live camera or stream as input and must keep pace with it in real time. Each specification table gives three hardware tiers — Minimum, the smallest setup on which the model runs correctly; Medium, the usual production configuration; and High, a configuration sized for peak volume. The rate is how many camera streams or feeds one server of that tier can keep up with in real time, not a per-hour count: live work must fit inside the interval between frames, and a server that cannot keep pace drops frames rather than falling behind. Use these rates for initial sizing. Before production, benchmark your own data to validate accuracy, latency, throughput and cost. The sample input here is one 1080p stream at 25 frames per second.
VideoSeal
Vendor: Meta
What it does: embeds a mark across frames that survives re-encoding, cropping and frame rate change, and can be read from a few seconds of recording. Built for video specifically and the default here.
Requirement
Minimum
Medium
High
GPU type
RTX 4090
A100 80 GB
2× A100 80 GB
VRAM
20 GB
80 GB
160 GB combined
vCPUs
8
16
32
RAM
32 GB
64 GB
128 GB
Server
1× RTX 4090 24 GB
1× A100 SXM 80 GB
2× A100 SXM 80 GB
Rate (camera streams handled at 25 frames per second)
≈ 2 streams
≈ 8 streams
≈ 20 streams
TrustMark
Vendor: Adobe Research
What it does: embeds a per-frame mark so a single screenshot still carries the payload. Heavier per frame, so it is applied to sampled frames rather than all of them.
Requirement
Minimum
Medium
High
GPU type
RTX 3090
RTX 4090
A100 80 GB
VRAM
12 GB
24 GB
80 GB
vCPUs
8
12
24
RAM
32 GB
48 GB
96 GB
Server
1× RTX 3090 24 GB
1× RTX 4090 24 GB
1× A100 SXM 80 GB
Rate (camera streams handled at 25 frames per second)
≈ 1 stream
≈ 4 streams
≈ 10 streams
HiDDeN
Vendor: Stanford University
What it does: a very small embedding model that fits comfortably inside a live frame budget, carrying a short payload with good resistance to recompression.
Requirement
Minimum
Medium
High
GPU type
RTX 3060
RTX 4090
A100 80 GB
VRAM
8 GB
24 GB
80 GB
vCPUs
6
12
24
RAM
16 GB
32 GB
64 GB
Server
1× RTX 3060 12 GB
1× RTX 4090 24 GB
1× A100 SXM 80 GB
Rate (camera streams handled at 25 frames per second)
≈ 6 streams
≈ 20 streams
≈ 50 streams
StegaStamp
Vendor: University of California, Berkeley
What it does: embeds a mark that survives being filmed off a screen, which is the route by which most live streams actually leak.
Requirement
Minimum
Medium
High
GPU type
RTX 3090
RTX 4090
A100 80 GB
VRAM
12 GB
24 GB
80 GB
vCPUs
8
12
24
RAM
32 GB
48 GB
96 GB
Server
1× RTX 3090 24 GB
1× RTX 4090 24 GB
1× A100 SXM 80 GB
Rate (camera streams handled at 25 frames per second)
≈ 2 streams
≈ 8 streams
≈ 18 streams
invisible-watermark (DWT-DCT-SVD)
Vendor: community-maintained open-source project
What it does: a classical method needing no GPU, adequate where the stream will be recorded but not screen-filmed, and effectively free.
Requirement
Minimum
Medium
High
GPU type
No GPU required
No GPU required
No GPU required
VRAM
—
—
—
vCPUs
2
8
32
RAM
4 GB
16 GB
64 GB
Server
CPU instance, 2 vCPU
CPU instance, 8 vCPU
CPU instance, 32 vCPU
Rate (camera streams handled at 25 frames per second)
≈ 30 streams
≈ 100 streams
≈ 300 streams
AudioSeal
Vendor: Meta
What it does: marks the audio track as well, so the payload survives even if the picture is replaced or the stream is stripped to sound.
Requirement
Minimum
Medium
High
GPU type
RTX 3060
RTX 4090
A100 80 GB
VRAM
8 GB
24 GB
80 GB
vCPUs
6
12
24
RAM
16 GB
32 GB
64 GB
Server
1× RTX 3060 12 GB
1× RTX 4090 24 GB
1× A100 SXM 80 GB
Rate (camera streams handled at 25 frames per second)
≈ 20 streams
≈ 70 streams
≈ 180 streams
FFmpeg
Vendor: FFmpeg project
What it does: handles decoding and re-encoding around the embedding step, and is where a badly chosen bitrate destroys the mark just applied.
Requirement
Minimum
Medium
High
GPU type
No GPU required
No GPU required
GPU-accelerated decode (NVENC/NVDEC)
VRAM
—
—
8 GB
vCPUs
2
8
16
RAM
4 GB
16 GB
32 GB
Server
CPU instance, 2 vCPU
CPU instance, 8 vCPU
1× RTX 4090 24 GB
Rate (camera streams handled at 25 frames per second)
≈ 12 streams
≈ 40 streams
≈ 120 streams
Choosing between them
The right method depends on your payload, the frame rate and resolution, and what the stream will be recorded or recompressed with. Our consultants review your distribution path, then recommend a method and report the measured recovery rate through your own encoding chain.
At the start of a project we may run a short proof of concept on a sample of your own data, measuring the accuracy and the throughput the model actually achieves on your material. That replaces the estimates on this page with real figures, so the cost and the schedule for the full engagement are known before it is committed.
Share a representative sample, expected volume, latency target and deployment location for Live steganography - hide small text/data in video frames. We benchmark the shortlisted models, recommend the lowest-cost GPU configuration that meets the target, and scale it from pilot capacity to a dedicated production cluster.